Systems and methods for image processing

ABSTRACT

A method may include obtaining an image of a subject and determining a plurality of image blocks in the image. The method may also include extracting grayscale features from each of the plurality of image blocks and determining, based on the grayscale features, a segmentation threshold. The method may further include segmenting, based on the segmentation threshold, the image.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of International Application No.PCT/CN2020/091969, filed on May 24, 2020, which claims priority toChinese Patent Application No. 201910840699.5, filed on Sep. 6, 2019,Chinese Patent Application No. 20190850220.6, filed on Sep. 9, 2019,Chinese Patent Application No. 201911008158.2, filed on Oct. 22, 2019,and Chinese Patent Application No. 201910441812.2, filed on May 24,2019, the contents of each of which are hereby incorporated byreference.

TECHNICAL FIELD

The disclosure generally relates to medical systems and methods, andmore particularly relates to medical systems and methods for imageprocessing.

BACKGROUND

A medical imaging system, such as an X-ray imaging device has beenwidely used in clinical examinations and medical diagnoses in recentyears. The X-ray imaging device (e.g., a digital radiography (DR), acomputed tomography (CT) device, etc.) may scan an object usingradiation rays and generate one or more images relating to the object.Before the screening and/or diagnosis of disease, one or more imageprocessing operations may be performed on an image relating to theobject. For example, an image segmentation operation may be performed toidentify a target region (e.g., a breast region) and/or a backgroundregion from an image which may allow a detection range of a lesionwithin the target region and/or reduce an effect of the backgroundregion on the target region. As another example, an image equalizationoperation may be performed to equalize gray values in an image, therebyimproving the contrast of the image. Thus, it is desired to providesystems and methods for image processing with improved accuracy and/orefficiency.

SUMMARY

According to a first aspect of the present disclosure, a system isprovided. The system may include at least one storage device storingexecutable instructions, and at least one processor in communicationwith the at least one storage device. When executing the executableinstructions, the at least one processor may cause the system to performone or more of the following operations. The system may obtain an imageof a subject and determine a plurality of image blocks in the image. Thesystem may also extract grayscale features from each of the plurality ofimage blocks and determine, based on the grayscale features, asegmentation threshold. The system may further segment, based on thesegmentation threshold, the image.

In some embodiments, to determine a plurality of image blocks in theimage, the system may determine a first count of first lines along afirst direction, determine a second count of second lines along a seconddirection; and divide, based on the first count of first lines and thesecond count of second lines, the image into the plurality of imageblocks.

In some embodiments, the grayscale features may include a grayscalefeature of a first type and a grayscale feature of a second type. And todetermine, based on the grayscale features, a segmentation threshold,the system may determine, based on the grayscale features extracted fromeach of the plurality of image blocks, a relationship between thegrayscale feature of the first type and the grayscale feature of thesecond type. The system may also determine, based on the relationship,the segmentation threshold.

In some embodiments, the grayscale feature of the first type may includea mean or median of gray values of pixels in an image block, and thegrayscale feature of the second type may include a standard deviation ora variance of the gray values of the pixels in the image block.

In some embodiments, to determine a relationship between the grayscalefeature of the first type and the grayscale feature of the second type,the system may determine, based on the grayscale features, a pluralityof points in a coordinate system associated with the grayscale featureof the first type and the grayscale feature of the second type,coordinates of each of the plurality of points representing thegrayscale features extracted from one of the plurality of image blocks;and determine, based on at least a portion of the plurality of points,the relationship between the grayscale feature of the first type and thegrayscale feature of the second type using a fitting technique.

In some embodiments, the segmenting, based on the segmentationthreshold, the image may include determining one or more target regionsfrom the images.

In some embodiments, the fitting technique may be determined based on acount of the one or more target regions.

In some embodiments, the system may determine the at least a portion ofthe plurality of points by performing at least one of a downsamplingoperation or an operation for removing abnormal points.

In some embodiments, for removing abnormal points, the system maydetermine a range of grayscale features of the first type of theplurality of points and divide the range of the grayscale features ofthe first type into multiple sub-ranges. For each of the multiplesub-ranges, the system may determine a first mean of grayscale featuresof the second type of points each of whose grayscale feature of thefirst type is in the sub-range, and classify, based on the first mean,the points into multiple groups. The system may also determine a secondmean of grayscale features of the second type of points in each of themultiple groups and determine, based on the second mean, the abnormalpoints.

In some embodiments, the multiple groups may include a first group and asecond group. The first group may include points each of whose grayscalefeature of the second type exceeds the first mean, and the second groupmay include points each of whose grayscale feature of the second type isless than the first mean. An abnormal point in the first group mayinclude the grayscale feature of the second type that exceeds the secondmean; and an abnormal point in the second group may include thegrayscale feature of the second type that is less than the second mean.

In some embodiments, to determine, based on the relationship, thesegmentation threshold, the system may determine, based on therelationship, a value of the grayscale feature of the first type whenthe grayscale feature of the second type is minimum or maximum, anddesignate the value of the grayscale feature of the first type as thesegmentation threshold.

In some embodiments, the segmenting, based on the segmentationthreshold, the image may include determining, based on the segmentationthreshold, a target region that includes a representation of thesubject.

In some embodiments, the system may determine a distribution of grayvalues of pixels in the target region, the distribution indicating acount of pixels corresponding to each gray value in the target region.The system may also determine, based on the distribution of the grayvalues of the pixels in the target region, a characteristic displayparameter for the target region, and display, based on thecharacteristic display parameter, the image.

In some embodiments, the characteristic display parameter may include awindow width or a window level for the target region.

In some embodiments, to determine, based on the distribution of the grayvalues of the pixels in the target region, the at least one of a windowwidth or a window level for the target region, the system may perform aniterative process including a plurality of iterations. For eachiteration of the iterative process, the system may determine anaccumulated count of pixels in a current iteration by summing a count ofpixels corresponding to a current gray value with an accumulated countof pixels determined in a prior iteration, and determine whether theaccumulated count of pixels in the current iteration satisfies a currentcondition. In response to determining that the accumulated count ofpixels in the current iteration does not satisfy the condition, thesystem may designate a gray value in the target region as the currentgray value; and update the current condition. The system may terminatethe iterative process until the accumulated count of pixels in thecurrent iteration satisfies the current condition, and determine, basedat least in part on the current gray value, the at least one of thewindow width or the window level for the target region.

In some embodiments, the distribution of the gray values of the pixelsin the target region may include a grayscale histogram that includes atransverse axis denoting the gray values and a vertical axis denoting acount of pixels corresponding to each of the gray values.

In some embodiments, to perform an iterative process, the system maydivide the grayscale histogram along the vertical axis into multipleintervals, each of the multiple intervals including a range of counts ofpixels of certain gray values.

In some embodiments, the designating a gray value in the target regionas the current gray value may include determining the gray valueaccording to at least one of a first rule or a second rule. The firstrule may relate to determining an interval from the multiple intervalsfrom a bottom interval to a top interval along the vertical axis insequence, and the second rule may relate to determining the gray valuein the interval from two sides to a center of the transverse axis.

In some embodiments, the system may determine the gray value from grayvalues in the interval from a left side to the center of the transverseaxis in sequence; or determine the gray value from the gray values inthe interval from a right side to the center of the transverse axis insequence when the current gray value is a maximum gray value that isless than a gray value corresponding to the center of the transverseaxis in the one of the multiple intervals.

In some embodiments, to determine, based at least in part on the currentgray value, the system may determine that the current gray value islocated on the right side of the center of the transverse axis. Thesystem may further designate the current gray value as a first end ofthe window width if the current gray value is located at the right sideof the center of the transverse axis, and designate a gray valuecorresponding to a same interval as the current gray value that islocated on the left side of the center of the transverse axis as asecond end of the window width.

In some embodiments, to determine, based at least in part on the currentgray value, the at least one of the window width or the window level forthe target region, the system may determine that the current gray valueis located on the right side of the center of the transverse axis. Thesystem may also designate the current gray value as a first end of thewindow width if the current gray value is located at the right side ofthe center of the transverse axis, and designate a gray valuecorresponding to a same interval as the current gray value that islocated on the left side of the center of the transverse axis as asecond end of the window width.

In some embodiments, to determine, based at least in part on the currentgray value, the at least one of the window width or the window level forthe target region, the system may determine that the current gray valueis located on a left side of the center of the transverse axis,designate the current gray value as a first end of the window width ifthe current gray value is located on the left side of the center of thetransverse axis, and designate a gray value corresponding to a sameinterval as the current gray value as a second end of the window width,the second end of the window width being located on a right side of thecenter of the transverse axis.

In some embodiments, the current condition may include a countthreshold.

In some embodiments, the updating the current condition may includedecreasing, based on a range of the gray values in the grayscalehistogram, a total count of pixels in the grayscale histogram, and aninterval corresponding to the current gray value, and a count of themultiple intervals, the count threshold.

In some embodiments, to perform an iterative process, the system maydivide the grayscale histogram along the transverse axis into multipleintervals, each of the multiple intervals including a range of grayvalues and a count of pixels corresponding to each gray value in therange.

In some embodiments, the designating a gray value in the target regionas the current gray value may include determining the gray valueaccording to at least one of a first rule or a second rule. The firstrule may relate to determining an interval from the multiple intervalsfrom two sides to a center of the transverse axis in sequence, and thesecond rule may relate to determining the gray value in the interval.

In some embodiments, the designating a gray value in the target regionas the current gray value may include determining the gray value fromgray values in a first interval of the multiple intervals from a leftside to the center of the transverse axis in sequence, or determiningthe gray value from gray values in a second interval of the multipleintervals from a right side to the center of the transverse axis whenthe current gray value is a maximum gray value in the first interval.

According to another aspect of the present disclosure, a method isprovided. The method may include obtaining an image of a subject anddetermining a plurality of image blocks in the image. The method mayalso include extracting grayscale features from each of the plurality ofimage blocks and determining, based on the grayscale features, asegmentation threshold. The method may further include segmenting, basedon the segmentation threshold, the image.

According to another aspect of the present disclosure, a non-transitorycomputer readable medium is provided. The non-transitory computerreadable medium storing instructions, the instructions, when executed bya computer, may cause the computer to implement a method. The method mayinclude obtaining an image of a subject and determining a plurality ofimage blocks in the image. The method may also include extractinggrayscale features from each of the plurality of image blocks anddetermining, based on the grayscale features, a segmentation threshold.The method may further include segmenting, based on the segmentationthreshold, the image.

According to another aspect of the present disclosure, a system isprovided. The system may include an acquisition module configured toobtain an image of a subject, an image block determination moduleconfigured to determine a plurality of image blocks in the image, agrayscale feature extraction module configured to extract grayscalefeatures from each of the plurality of image blocks, a segmentationthreshold determination module configured to determine, based on thegrayscale features, a segmentation threshold, and a segmentationthreshold configured to segment, based on the segmentation threshold,the image.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities, andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. The drawings are not scaled. Theseembodiments are non-limiting exemplary embodiments, in which likereference numerals represent similar structures throughout the severalviews of the drawings, and wherein:

FIG. 1A is a schematic diagram illustrating an exemplary system forprocessing image according to some embodiments of the presentdisclosure;

FIG. 1B is a schematic structural diagram illustrating an exemplaryX-ray system for image segmentation according to some embodiments of thepresent disclosure;

FIG. 2 is a schematic diagram illustrating hardware and/or softwarecomponents of an exemplary computing device 200 on which the processingdevice 120 may be implemented according to some embodiments of thepresent disclosure;

FIG. 3 is a schematic diagram illustrating hardware and/or softwarecomponents of an exemplary mobile device 300 according to someembodiments of the present disclosure;

FIG. 4 is a schematic diagram illustrating an exemplary processingdevice for an image segmentation according to some embodiments of thepresent disclosure;

FIG. 5 is a flowchart illustrating an exemplary process for segmentingan image according to some embodiments of the present disclosure;

FIG. 6 is a flowchart illustrating an exemplary process for determininga relationship between grayscale features according to some embodimentsof the present disclosure;

FIG. 7 is a schematic diagram illustrating exemplary image blocks in animage according to some embodiments of the present disclosure;

FIG. 8 is a schematic diagram illustrating an exemplary fitted curveusing a quadratic fitting technique according to some embodiments of thepresent disclosure;

FIG. 9 is a schematic diagram illustrating an exemplary processingdevice for image segmentation according to some embodiments of thepresent disclosure;

FIG. 10 is a flowchart illustrating an exemplary process for imagesegmentation according to some embodiments of the present disclosure;

FIG. 11 is a flowchart illustrating an exemplary process for determininga background region from the image according to some embodiments of thepresent disclosure;

FIG. 12 shows a grayscale histogram of the image according to someembodiments of the present disclosure;

FIG. 13 is a schematic diagram illustrating an exemplary processingdevice for determining image type according to some embodiments of thepresent disclosure;

FIG. 14 is a flowchart illustrating an exemplary process for determiningimage type according to some embodiments of the present disclosure;

FIG. 15 is an exemplary flowchart illustrating an exemplary process fordetermining image type according to some embodiments of the presentdisclosure;

FIG. 16 is a flowchart for an exemplary process for determining imagetype according to some embodiments of the present disclosure;

FIG. 17A is a schematic diagram illustrating an exemplary first imageaccording to some embodiments of the present disclosure;

FIG. 17B shows a first operation image of the first image as shown inFIG. 1A7A according to some embodiments of the present disclosure;

FIG. 17C shows a second operation image of the first image as shown inFIG. 1A7A according to some embodiments of the present disclosure;

FIGS. 18A-18B show third images obtained based on the first operationimage and the second operation image obtained in FIG. 1A7B and FIG. 1A7Caccording to some embodiments of the present disclosure;

FIG. 19 and FIG. 20 show exemplary images of different types accordingto some embodiments of the present disclosure;

FIG. 21 is a schematic diagram illustrating an exemplary processingdevice for processing image according to some embodiments of the presentdisclosure;

FIG. 22 is a flowchart illustrating an exemplary process for imageequalization according to some embodiments of the present disclosure;

FIG. 23 is a flowchart illustrating an exemplary process for determiningmultiple ranges of gray values in an image according to some embodimentsof the present disclosure;

FIG. 24 is a flowchart illustrating an exemplary process for determininga local transform model for a low-frequency image according to someembodiments of the present disclosure;

FIG. 25 is a flowchart illustrating an exemplary process for imageequalization according to some embodiments of the present disclosure;

FIG. 26 shows a grayscale histogram of a low-frequency image accordingto some embodiments of the present disclosure;

FIG. 27 shows a target transform curve corresponding to the multipleranges according to some embodiments of the present disclosure;

FIG. 28 shows a target transform curve corresponding to the multipleranges according to some embodiments of the present disclosure;

FIG. 29 illustrates target images obtained based on transform curvescorresponding to the high-attenuation range according to someembodiments of the present disclosure;

FIG. 30 illustrates target images obtained based on transform curvescorresponding to the low-attenuation range according to some embodimentsof the present disclosure;

FIG. 31 is a schematic diagram illustrating an exemplary processingdevice for processing image according to some embodiments of the presentdisclosure;

FIG. 32 is a flowchart illustrating an exemplary process for processingan image according to some embodiments of the present disclosure;

FIG. 33 is a schematic diagram illustrating an exemplary processingdevice for processing an image according to some embodiments of thepresent disclosure;

FIG. 34 is a flowchart illustrating an exemplary process for processingan image according to some embodiments of the present disclosure;

FIG. 35 is a flowchart illustrating an exemplary process for aniterative process for determining a window width or a window level forthe target region according to some embodiments of the presentdisclosure;

FIG. 36 shows an exemplary grayscale histogram that is divided along thevertical axis according to some embodiments of the present disclosure;

FIGS. 37 and 38 show an exemplary grayscale histogram according to someembodiments of the present disclosure; and

FIG. 39 shows an exemplary grayscale histogram that is divided along thetransverse axis according to some embodiments of the present disclosure.

DETAILED DESCRIPTION

The following description is presented to enable any person skilled inthe art to make and use the present disclosure and is provided in thecontext of a particular application and its requirements. Variousmodifications to the disclosed embodiments will be readily apparent tothose skilled in the art, and the general principles defined herein maybe applied to other embodiments and applications without departing fromthe spirit and scope of the present disclosure. Thus, the presentdisclosure is not limited to the embodiments shown but is to be accordedthe widest scope consistent with the claims.

The terminology used herein is for the purpose of describing particularexample embodiments only and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprise,”“comprises,” and/or “comprising,” “include,” “includes,” and/or“including” when used in this disclosure, specify the presence of statedfeatures, integers, steps, operations, elements, and/or components, butdo not preclude the presence or addition of one or more other features,integers, steps, operations, elements, components, and/or groupsthereof.

Generally, the word “module,” “unit,” or “block,” as used herein, refersto logic embodied in hardware or firmware, or to a collection ofsoftware instructions. A module, a unit, or a block described herein maybe implemented as software and/or hardware and may be stored in any typeof non-transitory computer-readable medium or other storage devices. Insome embodiments, a software module/unit/block may be compiled andlinked into an executable program. It will be appreciated that softwaremodules can be callable from other modules/units/blocks or themselves,and/or may be invoked in response to detected events or interrupts.Software modules/units/blocks configured for execution on computingdevices may be provided on a computer-readable medium, such as a compactdisc, a digital video disc, a flash drive, a magnetic disc, or any othertangible medium, or as a digital download (and can be originally storedin a compressed or installable format that needs installation,decompression, or decryption prior to execution). Such software code maybe stored, partially or fully, on a storage device of the executingcomputing device, for execution by the computing device. Softwareinstructions may be embedded in firmware, such as an erasableprogrammable read-only memory (EPROM). It will be further appreciatedthat hardware modules/units/blocks may be included in connected logiccomponents, such as gates and flip-flops, and/or can be included ofprogrammable units, such as programmable gate arrays or processors. Themodules/units/blocks or computing device functionality described hereinmay be implemented as software modules/units/blocks but may berepresented in hardware or firmware. In general, themodules/units/blocks described herein refer to logicalmodules/units/blocks that may be combined with othermodules/units/blocks or divided into sub-modules/sub-units/sub-blocksdespite their physical organization or storage. The description may beapplicable to a system, an engine, or a portion thereof.

It will be understood that the term “system,” “engine,” “unit,”“module,” and/or “block” used herein are one method to distinguishdifferent components, elements, parts, sections, or assembly ofdifferent levels in ascending order. However, the terms may be displacedby another expression if they achieve the same purpose.

It will be understood that when a unit, engine, module or block isreferred to as being “on,” “connected to,” or “coupled to,” anotherunit, engine, module, or block, it may be directly on, connected orcoupled to, or communicate with the other unit, engine, module, orblock, or an intervening unit, engine, module, or block may be presentunless the context clearly indicates otherwise. As used herein, the term“and/or” includes any and all combinations of one or more of theassociated listed items.

These and other features, and characteristics of the present disclosure,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, may become more apparent upon consideration of thefollowing description regarding the accompanying drawings, all of whichform a part of this disclosure. It is to be expressly understood,however, that the drawings are for the purpose of illustration anddescription only and are not intended to limit the scope of the presentdisclosure. It is understood that the drawings are not to scale.

The flowcharts used in the present disclosure illustrate operations thatsystems implement according to some embodiments in the presentdisclosure. It is to be expressly understood, the operations of theflowchart may be implemented not in order. Conversely, the operationsmay be implemented in an inverted order, or simultaneously. Moreover,one or more other operations may be added to the flowcharts. One or moreoperations may be removed from the flowcharts.

FIG. 1A is a schematic diagram illustrating an exemplary medical systemaccording to some embodiments of the present disclosure. In someembodiments, the medical system 100 may be a single-modality system or amulti-modality system. Exemplary single-modality systems may include amagnetic resonance (MR) system, an X-ray mammography system, a digital Xradiographic system (e.g., a computed tomography (CT) system, a digitalradiography (DR) system, etc.), a positron emission tomography (PET)system, a single-photon emission computed tomography (SPECT) system,etc. Exemplary X-ray mammography system may include a breast dry plateX-ray photography system, a film-screen mammography system, a digitalradiography (DR) system (e.g. a full-field digital mammography (FFDM), adigital breast tomosynthesis (DBT) system, a phase contrast mammography(PCM) system, a computed radiography (CR) system, a multi-modalitysystem, or the like, or a combination thereof. Exemplary multi-modalitysystems may include an MR-CT system, a PET-CT system, etc. In someembodiments, the medical system 100 may include modules and/orcomponents for performing imaging and/or related analysis. It should benoted that the descriptions of an imaging system in the presentdisclosure are merely provided for illustration, and not intended tolimit the scope of the present disclosure.

Merely by way of example, as illustrated in FIG. 1A, the medical system100 may include a medical device 110, a processing device 120, a storagedevice 130, one or more terminals 140, and a network 150. The componentsin the medical system 100 may be connected in one or more of variousways. Merely by way of example, the medical device 110 may be connectedto the processing device 120 through the network 150. As anotherexample, the medical device 110 may be connected to the processingdevice 120 directly as illustrated in FIG. 1A. As a further example, theterminal(s) 140 may be connected to another component of the medicalsystem 100 (e.g., the processing device 120) via the network 150. Asstill a further example, the terminal(s) 140 may be connected to theprocessing device 120 directly as illustrated by the dotted arrow inFIG. 1A. As still a further example, the storage device 130 may beconnected to another component of the medical system 100 (e.g., theprocessing device 120) directly as illustrated in FIG. 1A, or throughthe network 150.

The medical device 110 may be configured to acquire image data relatingto at least one part of a subject. For example, the medical device 110may be configured to scan an object using radiation rays and generateimaging data used to generate one or more images relating to the object.In some embodiments, the medical device 110 may transmit the imagingdata to the processing device 120 for further processing (e.g.,generating one or more images). In some embodiments, the imaging dataand/or the one or more images associated with the object may be storedin the storage device 130 and/or the processing device 120.

In some embodiments, the medical device 110 may be a CT scanner, asuspended X-ray imaging device, a DR scanner (e.g., a mobile digitalX-ray imaging device), a digital subtraction angiography (DSA) scanner,a dynamic spatial reconstruction (DSR) scanner, an X-ray microscopyscanner, a multimodality scanner, or the like, or a combination thereof.Exemplary multi-modality scanners may include a computedtomography-positron emission tomography (CT-PET) scanner, a computedtomography-magnetic resonance imaging (CT-MRI) scanner, etc. The objectmay be biological or non-biological. Merely by way of example, theobject may include a patient, a man-made object, etc. As anotherexample, the object may include a specific portion, organ, and/or tissueof a patient. For example, the object may include the head, the brain,the neck, a body, a shoulder, an arm, the thorax, the heart, thestomach, blood vessels, soft tissues, a knee, feet, or the like, or anycombination thereof.

The processing device 120 may process data and/or information obtainedfrom the medical device 110, the terminal(s) 140, and/or the storagedevice 130. In some embodiments, the processing device 120 may acquirean image and perform image segmentation on the image. For example, theprocessing device 120 may determine a plurality of image blocks in theimage and extract grayscale features from each of the plurality of imageblocks. The processing device 120 may further determine a segmentationthreshold based on the grayscale features and segment the image based onthe segmentation threshold. As another example, the processing device120 may determine a first segmentation threshold based on the image anddetermine a second segmentation threshold based on the firstsegmentation threshold and gray values of pixels in the image. Theprocessing device 120 may further segment the image based on the secondsegmentation threshold.

In some embodiments, the processing device 120 may perform an imageequalization operation on the image. For example, the processing device120 may determine, based on the image of the subject, a first image anda second image, the first image representing low-frequency informationin the image, and the second image representing high-frequencyinformation in the image. The processing device 120 may determinemultiple ranges of gray values represented in the first image anddetermine, based at least in part on the multiple ranges of the grayvalues of the pixels in the first image, a local transform model. Theprocessing device 120 may determine a third image by processing thefirst image using the local transform model and determine, based on thethird image and the second image, an equalized image of the subject.

In some embodiments, the processing device 120 may determine a type ofthe image. For example, the processing device 120 may obtain a firstimage and obtain a second image based on the first image. The processingdevice 120 may determine one or more image blocks in the second image.The processing device 120 may further determine at least one statisticresult associated with a pixel parameter of a type for pixels in each ofthe one or more image blocks. The processing device 120 further maydetermine a type of the first image based at least in part on the atleast one statistic result.

In some embodiments, the processing device 120 may process an imageusing a trained machine learning model that is obtained by training amachine learning model using a plurality of training samples obtainedfrom a sample set. The trained machine learning model used in thepresent disclosure (e.g., the trained machine learning model) may beupdated from time to time, e.g., periodically or not, based on a sampleset that is at least partially different from the original sample setfrom which the original trained machine learning model is determined.For instance, the trained machine learning model (e.g., the trainedmachine learning model) may be updated based on a sample set includingnew samples that are not in the original sample set. In someembodiments, the determination and/or updating of the trained machinelearning model (e.g., the trained machine learning model) may beperformed on a processing device, while the application of the trainedmachine learning model may be performed on a different processingdevice. In some embodiments, the determination and/or updating of thetrained machine learning model (e.g., the trained machine learningmodel) may be performed on a processing device of a system differentthan the medical system 100 or a server different than a serverincluding the processing device 120 on which the application of thetrained machine learning model is performed. For instance, thedetermination and/or updating of the trained machine learning model(e.g., the trained machine learning model) may be performed on a firstsystem of a vendor who provides and/or maintains such a machine learningmodel and/or has access to training samples used to determine and/orupdate the trained machine learning model, while the application of thetrained machine learning model may be performed on a second system of aclient of the vendor. In some embodiments, the determination and/orupdating of the trained machine learning model (e.g., the trainedmachine learning model) may be performed online in response to a requestfor image generation. In some embodiments, the determination and/orupdating of the trained machine learning model may be performed offline.

In some embodiments, the processing device 120 may be a computer, a userconsole, a single server or a server group, etc. The server group may becentralized or distributed. In some embodiments, the processing device120 may be local or remote. For example, the processing device 120 mayaccess information and/or data stored in the medical device 110, theterminal(s) 140, and/or the storage device 130 via the network 150. Asanother example, the processing device 120 may be directly connected tothe medical device 110, the terminal(s) 140, and/or the storage device130 to access stored information and/or data. In some embodiments, theprocessing device 120 may be implemented on a cloud platform. Merely byway of example, the cloud platform may include a private cloud, a publiccloud, a hybrid cloud, a community cloud, a distributed cloud, aninter-cloud, a multi-cloud, or the like, or any combination thereof.

The storage device 130 may store data, instructions, and/or any otherinformation. In some embodiments, the storage device 130 may store dataobtained from the terminal(s) 140 and/or the processing device 120. Thedata may include image data acquired by the processing device 120,algorithms and/or models for processing the image data, etc. Forexample, the storage device 130 may store image data (e.g., MR images,image data of the subject, etc.) acquired by the medical device 110. Asanother example, the storage device 130 may store one or more algorithmsfor processing the image data. In some embodiments, the storage device130 may store data and/or instructions that the processing device 120may execute or use to perform exemplary methods/systems described in thepresent disclosure. In some embodiments, the storage device 130 mayinclude a mass storage, removable storage, a volatile read-and-writememory, a read-only memory (ROM), or the like, or any combinationthereof. Exemplary mass storage may include a magnetic disk, an opticaldisk, a solid-state drive, etc. Exemplary removable storage may includea flash drive, a floppy disk, an optical disk, a memory card, a zipdisk, a magnetic tape, etc. Exemplary volatile read-and-write memoriesmay include a random access memory (RAM). Exemplary RAM may include adynamic RAM (DRAM), a double date rate synchronous dynamic RAM (DDRSDRAM), a static RAM (SRAM), a thyristor RAM (T-RAM), and azero-capacitor RAM (Z-RAM), etc. Exemplary ROM may include a mask ROM(MROM), a programmable ROM (PROM), an erasable programmable ROM (EPROM),an electrically erasable programmable ROM (EEPROM), a compact disk ROM(CD-ROM), and a digital versatile disk ROM, etc. In some embodiments,the storage device 130 may be implemented on a cloud platform. Merely byway of example, the cloud platform may include a private cloud, a publiccloud, a hybrid cloud, a community cloud, a distributed cloud, aninter-cloud, a multi-cloud, or the like, or any combination thereof.

In some embodiments, the storage device 130 may be connected to thenetwork 150 to communicate with one or more other components in themedical system 100 (e.g., the processing device 120, the terminal(s)140, etc.). One or more components in the medical system 100 may accessthe data or instructions stored in the storage device 130 via thenetwork 150. In some embodiments, the storage device 130 may be directlyconnected to or communicate with one or more other components in themedical system 100 (e.g., the processing device 120, the terminal(s)140, etc.). In some embodiments, the storage device 130 may be part ofthe processing device 120.

The terminal(s) 140 may include a mobile device 141, a tablet computer142, a laptop computer 143, or the like, or any combination thereof. Insome embodiments, the mobile device 141 may include a smart home device,a wearable device, a mobile device, a virtual reality device, anaugmented reality device, or the like, or any combination thereof. Insome embodiments, the smart home device may include a smart lightingdevice, a control device of an intelligent electrical apparatus, a smartmonitoring device, a smart television, a smart video camera, aninterphone, or the like, or any combination thereof. In someembodiments, the wearable device may include a bracelet, a footgear,eyeglasses, a helmet, a watch, clothing, a backpack, a smart accessory,or the like, or any combination thereof. In some embodiments, the mobiledevice may include a mobile phone, a personal digital assistant (PDA), agaming device, a navigation device, a point of sale (POS) device, alaptop, a tablet computer, a desktop, or the like, or any combinationthereof. In some embodiments, the virtual reality device and/or theaugmented reality device may include a virtual reality helmet, virtualreality glasses, a virtual reality patch, an augmented reality helmet,augmented reality glasses, an augmented reality patch, or the like, orany combination thereof. For example, the virtual reality device and/orthe augmented reality device may include a Google Glass™, an OculusRift™, a Hololens™, a Gear VR™, etc. In some embodiments, theterminal(s) 140 may be part of the processing device 120.

The network 150 may include any suitable network that can facilitate theexchange of information and/or data for the medical system 100. In someembodiments, one or more components of the medical device 110 (e.g., anMRI device), the terminal(s) 140, the processing device 120, the storagedevice 130, etc., may communicate information and/or data with one ormore other components of the medical system 100 via the network 150. Forexample, the processing device 120 may obtain data from the medicaldevice 110 via the network 150. As another example, the processingdevice 120 may obtain user instructions from the terminal(s) 140 via thenetwork 150. The network 150 may be and/or include a public network(e.g., the Internet), a private network (e.g., a local area network(LAN), a wide area network (WAN)), etc.), a wired network (e.g., anEthernet network), a wireless network (e.g., an 802.11 network, a Wi-Finetwork, etc.), a cellular network (e.g., a Long Term Evolution (LTE)network), a frame relay network, a virtual private network (“VPN”), asatellite network, a telephone network, routers, hubs, switches, servercomputers, and/or any combination thereof. Merely by way of example, thenetwork 150 may include a cable network, a wireline network, afiber-optic network, a telecommunications network, an intranet, awireless local area network (WLAN), a metropolitan area network (MAN), apublic telephone switched network (PSTN), a Bluetooth™ network, aZigBee™ network, a near field communication (NFC) network, or the like,or any combination thereof. In some embodiments, the network 150 mayinclude one or more network access points. For example, the network 150may include wired and/or wireless network access points such as basestations and/or internet exchange points through which one or morecomponents of the medical system 100 may be connected to the network 150to exchange data and/or information.

It should be noted that the above description of the medical system 100is merely provided for illustration, and not intended to limit the scopeof the present disclosure. For persons having ordinary skills in theart, multiple variations and modifications may be made under theteachings of the present disclosure. For example, the assembly and/orfunction of the medical system 100 may be varied or changed according tospecific implementation scenarios.

FIG. 1B, is a schematic structural diagram illustrating an exemplaryX-ray imaging device according to some embodiments of the presentdisclosure.

As shown in FIG. 1B, the X-ray medical device 110 may include aradiation source 102, a collimator 104, an electronic cassette 106, adetector 108, and a processing device (not shown in the figure).

The radiation source 102 may include a tube (not shown in FIG. 1B). Thetube may generate and/or emit radiation beams travelling toward theobject. The radiation may include a particle ray, a photon ray, or thelike, or a combination thereof. In some embodiments, the radiation mayinclude a plurality of radiation particles (e.g., neutrons, protons,electron, p-mesons, heavy ions), a plurality of radiation photons (e.g.,X-ray, a y-ray, ultraviolet, laser), or the like, or a combinationthereof. In some embodiments, the tube may include an anode target and afilament. The filament may be configured to generate electrons tobombard the anode target. The anode target may be configured to generatethe radiation rays (e.g., X-rays) when the electrons bombard the anodetarget. The collimator 104 may be configured to control the irradiationregion (i.e., radiation field) on the object. The collimator 104 mayalso be configured to adjust the intensity and/or the number of theradiation beams that irradiate on the object.

The detector 108 may detect radiation beams. For example, the detector108 may convert the received radiation rays (e.g., X-rays) into visiblelight and convert the visible light into an analog electrical signalthat represents the intensity of the received radiation rays (e.g.,X-rays).

The electronic cassette 106 may include one or more electronic circuitsconfigured to convert an analog electrical signal that represents theintensity of the received radiation rays (e.g., X-rays) to a digitalsignal and generate scan data (e.g., projection data) based on thedigital signal.

The processing device may be the same as or similar to the processingdevice 120 as described in FIG. 1A. For example, the processing device120 may determine a background region from an image acquired by theX-ray medical device 110.

FIG. 2 is a schematic diagram illustrating hardware and/or softwarecomponents of an exemplary computing device 200 on which the processingdevice 120 may be implemented according to some embodiments of thepresent disclosure. As illustrated in FIG. 2, the computing device 200may include a processor 210, a storage 220, an input/output (I/O) 230,and a communication port 240.

The processor 210 may execute computer instructions (program codes) andperform functions of the processing device 120 in accordance withtechniques described herein. The computer instructions may include, forexample, routines, programs, objects, components, signals, datastructures, procedures, modules, and functions, which perform particularfunctions described herein. For example, the processor 210 may processdata obtained from the medical device 110, the terminal(s) 140, thestorage device 130, and/or any other component of the medical system100. Specifically, the processor 210 may process one or more measureddata sets obtained from the medical device 110. For example, theprocessor 210 may generate an image based on the data set(s). In someembodiments, the generated image may be stored in the storage device130, the storage 220, etc. In some embodiments, the generated image maybe displayed on a display device by the I/O 230. In some embodiments,the processor 210 may perform instructions obtained from the terminal(s)140. In some embodiments, the processor 210 may include one or morehardware processors, such as a microcontroller, a microprocessor, areduced instruction set computer (RISC), an application-specificintegrated circuit (ASIC), an application-specific instruction-setprocessor (ASIP), a central processing unit (CPU), a graphics processingunit (GPU), a physics processing unit (PPU), a microcontroller unit, adigital signal processor (DSP), a field-programmable gate array (FPGA),an advanced RISC machine (ARM), a programmable logic device (PLD), anycircuit or processor capable of executing one or more functions, or thelike, or any combinations thereof.

Merely for illustration, only one processor is described in thecomputing device 200. However, it should be noted that the computingdevice 200 in the present disclosure may also include multipleprocessors. Thus operations and/or method steps that are performed byone processor as described in the present disclosure may also be jointlyor separately performed by the multiple processors. For example, if inthe present disclosure the processor of the computing device 200executes both operation A and operation B, it should be understood thatoperation A and operation B may also be performed by two or moredifferent processors jointly or separately in the computing device 200(e.g., a first processor executes operation A and a second processorexecutes operation B, or the first and second processors jointly executeoperations A and B).

The storage 220 may store data/information obtained from the medicaldevice 110, the terminal(s) 140, the storage device 130, or any othercomponent of the medical system 100. In some embodiments, the storage220 may include a mass storage device, a removable storage device, avolatile read-and-write memory, a read-only memory (ROM), or the like,or any combination thereof. For example, the mass storage may include amagnetic disk, an optical disk, a solid-state drive, etc. The removablestorage may include a flash drive, a floppy disk, an optical disk, amemory card, a zip disk, a magnetic tape, etc. The volatileread-and-write memory may include a random access memory (RAM). The RAMmay include a dynamic RAM (DRAM), a double date rate synchronous dynamicRAM (DDR SDRAM), a static RAM (SRAM), a thyristor RAM (T-RAM), and azero-capacitor RAM (Z-RAM), etc. The ROM may include a mask ROM (MROM),a programmable ROM (PROM), an erasable programmable ROM (PEROM), anelectrically erasable programmable ROM (EEPROM), a compact disk ROM(CD-ROM), and a digital versatile disk ROM, etc. In some embodiments,the storage 220 may store one or more programs and/or instructions toperform exemplary methods described in the present disclosure.

The I/O 230 may input or output signals, data, and/or information. Insome embodiments, the I/O 230 may enable user interaction with theprocessing device 120. In some embodiments, the I/O 230 may include aninput device and an output device. Exemplary input devices may include akeyboard, a mouse, a touch screen, a microphone, or the like, or acombination thereof. Exemplary output devices may include a displaydevice, a loudspeaker, a printer, a projector, or the like, or acombination thereof. Exemplary display devices may include a liquidcrystal display (LCD), a light-emitting diode (LED)-based display, aflat panel display, a curved screen, a television device, a cathode raytube (CRT), or the like, or a combination thereof.

The communication port 240 may be connected with a network (e.g., thenetwork 150) to facilitate data communications. The communication port240 may establish connections between the processing device 120 and themedical device 110, the terminal(s) 140, or the storage device 130. Theconnection may be a wired connection, a wireless connection, or acombination of both that enables data transmission and reception. Thewired connection may include an electrical cable, an optical cable, atelephone wire, or the like, or any combination thereof. The wirelessconnection may include a Bluetooth network, a Wi-Fi network, a WiMaxnetwork, a WLAN, a ZigBee network, a mobile network (e.g., 3G, 4G, 5G,etc.), or the like, or any combination thereof. In some embodiments, thecommunication port 240 may be a standardized communication port, such asRS232, RS485, etc. In some embodiments, the communication port 240 maybe a specially designed communication port. For example, thecommunication port 240 may be designed in accordance with the digitalimaging and communications in medicine (DICOM) protocol.

FIG. 3 is a schematic diagram illustrating hardware and/or softwarecomponents of an exemplary mobile device 300 according to someembodiments of the present disclosure. As illustrated in FIG. 3, themobile device 300 may include a communication platform 310, a display320, a graphics processing unit (GPU) 330, a central processing unit(CPU) 340, an I/O 350, a memory 360, and a storage 390. In someembodiments, any other suitable component, including but not limited toa system bus or a controller (not shown), may also be included in themobile device 300. In some embodiments, a mobile operating system 370(e.g., iOS, Android, Windows Phone, etc.) and one or more applications380 may be loaded into the memory 360 from the storage 390 to beexecuted by the CPU 340. The applications 380 may include a browser orany other suitable mobile apps for receiving and rendering informationrelating to image data acquisition or other information from theprocessing device 120. User interactions with the information stream maybe achieved via the I/O 350 and provided to the processing device 120and/or other components of the medical system 100 via the network 150.

To implement various modules, units, and functionalities described inthe present disclosure, computer hardware platforms may be used as thehardware platform(s) for one or more of the elements described herein.The hardware elements, operating systems and programming languages ofsuch computers are conventional, and it is presumed that those skilledin the art are adequately familiar therewith to adapt those technologiesfor image data acquisition as described herein. A computer with userinterface elements may be used to implement a personal computer (PC) oranother type of work station or terminal device, although a computer mayalso act as a server if appropriately programmed. It is believed thatthose skilled in the art are familiar with the structure, programming,and general operation of such computer equipment and as a result, thedrawings should be self-explanatory.

FIG. 4 is a schematic diagram illustrating an exemplary processingdevice for an image segmentation according to some embodiments of thepresent disclosure. As shown, the processing device 120 may include anacquisition module 402, an image block determination module 404, agrayscale feature extraction module 406, a segmentation thresholddetermination module 408, a segmentation module 410, and a storagemodule 412. In some embodiments, the acquisition module 402, the imageblock determination module 404, the grayscale feature extraction module406, the segmentation threshold determination module 408, thesegmentation module 410, and the storage module 412 may be connected toand/or communicate with each other via a wireless connection (e.g., anetwork), a wired connection, or a combination thereof.

The acquisition module 402 may be configured to obtain informationrelated to an image. The image may include a medical image. For example,the image may include a DR image, an MR image, a PET image, a CT image,etc. As another example, the subject may include a breast. In someembodiments, the acquisition module 402 may obtain the image from thestorage device 130, the storage module 412, a local storage device(e.g., a storage device implemented on the terminal device(s) 140 or themedical device 110) or other storage devices that be in communicationwith the processing device 120. In some embodiments, the acquisitionmodule 402 may obtain the image from an imaging device (e.g., themedical device 110).

The image block determination module 404 may be configured to determinea plurality of image blocks in the image. In some embodiments, the imageblock determination module 404 may determine a first count of firstlines (also referred to as first division lines) along a first directionand a second count of second lines (also referred to as second divisionlines) along a second direction. In some embodiments, the image blockdetermination module 404 may divide the image into the plurality ofimage blocks based on the first count of first lines and the secondcount of second lines.

The grayscale feature extraction module 406 may be configured to extractgrayscale features from each of the plurality of image blocks. As usedherein, a grayscale feature extracted from an image block may refer to astatistic associated with gray values of pixels in the image block.Exemplary statistics associated with gray values of pixels in an imageblock may include a mean, a median, a standard deviation, a variance,etc., of the gray values of the pixels in the image block.

In some embodiments, the grayscale features may include a grayscalefeature of a first type and a grayscale feature of a second type. Thegrayscale feature of the first type associated with gray values ofpixels in an image block may indicate a level or trend of the grayvalues of the pixels in the image block. The grayscale feature of thesecond type associated with gray values of pixels in an image block mayindicate a deviation between each of the gray values of pixels in theimage block and the grayscale feature of the first type (i.e., the levelor trend of the gray values of the pixels in the image block).

The segmentation threshold determination module 408 may be configured todetermine a segmentation threshold based on the grayscale features.

In some embodiments, the segmentation threshold determination module 408may determine a relationship between the grayscale feature of the firsttype and the grayscale feature of the second type based on the grayscalefeatures extracted from each of the plurality of image blocks. In someembodiments, the segmentation threshold determination module 408 mayfurther determine the segmentation threshold based on the relationship.

In some embodiments, the segmentation threshold determination module 408may determine, based on the relationship between the grayscale featureof the first type and the grayscale feature of the second type, aspecific value of the grayscale feature of the first type when thegrayscale feature of the second type is minimum or maximum locally orglobally. In some embodiments, the segmentation threshold determinationmodule 408 may designate the specific value of the grayscale feature ofthe first type as the segmentation threshold.

In some embodiments, the segmentation threshold determination module 408may determine, based on the grayscale features, a plurality of points ina coordinate system associated with the grayscale feature of the firsttype and the grayscale feature of the second type. Coordinates of eachof the plurality of points may represent the grayscale featuresextracted from one of the plurality of image blocks. In someembodiments, the segmentation threshold determination module 408 maydetermine, based on at least a portion of the plurality of points, therelationship between the grayscale feature of the first type and thegrayscale feature of the second type using a fitting technique.

In some embodiments, the segmentation threshold determination module 408may determine, based on gray features extracted from each of a pluralityof image blocks in an image, a plurality of points in a coordinatesystem associated with the gray feature of a first type and the grayfeature of a second type. In some embodiments, the segmentationthreshold determination module 408 may determine at least a portion ofthe plurality of points by performing at least one of a downsamplingoperation or an operation for removing abnormal points. In someembodiments, the segmentation threshold determination module 408 maydetermine a relationship between the grayscale feature of the first typeand the grayscale feature of the second type based on the at least aportion of the plurality of points. In some embodiments, thesegmentation threshold determination module 408 may determine thesegmentation threshold based on the relationship between the grayfeature of the first type and the gray feature of the second type.

The segmentation module 410 may be configured to segment the image basedon the segmentation threshold.

In some embodiments, the segmentation module 410 may segment the imageby determining one or more regions from the image based on thesegmentation threshold.

In some embodiments, the segmentation module 410 may determine thetarget region by comparing the gray values of the pixels in the imageand the segmentation threshold. In some embodiments, the segmentationmodule 410 may further perform an image equalization operation on theimage before segmenting the image.

The storage module 412 may be configured to store data, instructions,and/or any other information for an image segmentation. For example, thestorage module 412 may store the image of the subject, data of theplurality of image blocks, the grayscale features, etc., extracted fromthe plurality of image blocks. In some embodiments, the storage module412 may be the same as the storage device 130 in configuration.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. Apparently, for persons having ordinary skills inthe art, multiple variations and modifications may be conducted underthe teachings of the present disclosure. However, those variations andmodifications do not depart from the scope of the present disclosure.For example, the image block determination module 404 and the grayscalefeature extraction module 406 may be integrated into a single module. Asanother example, some other components/modules may be added into and/oromitted from the processing device 120.

FIG. 5 is a flowchart illustrating an exemplary process for segmentingan image according to some embodiments of the present disclosure. Insome embodiments, process 500 may be implemented as a set ofinstructions (e.g., an application) stored in the storage device 130,storage 220, or storage 390. The processing device 120, the processor210, and/or the CPU 340 may execute the set of instructions, and whenexecuting the instructions, the processing device 120, the terminal 140,the processor 210, and/or the CPU 340 may be configured to perform theprocess 500. The operations of the illustrated process presented beloware intended to be illustrative. In some embodiments, the process 500may be accomplished with one or more additional operations not describedand/or without one or more of the operations discussed. Additionally,the order of the operations of the process 500 illustrated in FIG. 5 anddescribed below is not intended to be limiting.

Process 500 may be used to image segmentation, for example, imagesegmentation of a medical image (e.g., a digital X-ray image). As usedherein, the image segmentation refers to classifying pixels in an imagebased on a pixel parameter of the type (e.g., a grayscale, a luminance,chrominance, etc.) and/or an image feature (e.g., a texture feature, ashape feature, a space feature, etc.), to distinguish different subjectsrepresented in the image and/or distinguish background and the one ormore subjects represented in the image. In some embodiments, the imagesegmentation may include segmenting or distinguishing a target region(e.g., a region representing at least a portion of a subject (e.g., abreast)) from a background region in an image (e.g., a medical image).In some embodiments, the image segmentation may include segmenting ordistinguishing different regions representing different portions of thesubject (e.g., tissues of different types), etc.

In 502, the processing device 120 (e.g., the acquisition module 402) mayobtain an image of a subject may be obtained.

In some embodiments, the image may include a medical image. For example,the image may include a DR image, an MR image, a PET image, a CT image,etc. The subject may be biological or non-biological. For example, thesubject may include a patient, a man-made object, etc. As anotherexample, the subject may include a specific portion, organ, and/ortissue of a patient. As still another example, the subject may include abreast. More descriptions for the subject may be found elsewhere in thepresent disclosure (e.g., FIG. 1A and the descriptions thereof).

In some embodiments, the processing device 120 may obtain the image fromthe storage device 130, the storage module 412, a local storage device(e.g., a storage device implemented on the terminal device(s) 140 or themedical device 110) or other storage devices that be in communicationwith the processing device 120.

In some embodiments, the processing device 120 may obtain the image froman imaging device (e.g., the medical device 110). The imaging device mayacquire the image after receiving an image acquisition instruction. Theimaging device may include a digital X radiographic device (e.g., a DRdevice, a CT device, etc.). For example, in response to receiving theimage acquisition instruction, a DR device may scan the subject viagenerating and emitting X-ray photons, and converting X-ray photonsabsorbed by the subject into scan data (e.g., projection data, theimage) using an electronic cassette (e.g., the chest radiograph cassette106 as shown in FIG. 1B). In some embodiments, the processing device 120may reconstruct the image based on the scan data.

In 504, the processing device 120 (e.g., the image block determinationmodule 404) may determine a plurality of image blocks in the image.

The sizes of at least two of the plurality of image blocks may be thesame or different. For example, the plurality of image blocks may havethe same size. The size of an image block may be defined by a length, awidth, and/or an area of the image block, and/or a count of pixels inthe image block.

In some embodiments, the processing device 120 may determine a firstcount of first lines (also referred to as first division lines) along afirst direction and a second count of second lines (also referred to assecond division lines) along a second direction. The processing device120 may divide the image into the plurality of image blocks based on thefirst count of first lines and the second count of second lines. As usedherein, a first line along the first direction refers to that the firstline is parallel with the first direction. A second line along thesecond direction refers to that the second line is parallel with thesecond direction.

In some embodiments, the first direction may be perpendicular to thesecond direction. For example, the first direction may include a rowdirection of the image and the second direction may include a columndirection of the image. As another example, the first direction mayinclude a horizontal direction and the second direction may include avertical direction.

In some embodiments, the first count and/or the second count may be setby a user or according to a default setting of the medical system 100.For example, the first count and/or the second count may be equal to 16,32, 64, etc. The first count and the second count may be the same ordifferent. For example, the first count and the second count may both be32.

In some embodiments, the first count and/or the second count may bedetermined according to the size of the image. The size of the image maybe defined by a length in the column direction of the image and/or awidth in the row direction of the image. For example, if the firstdirection includes the row direction of the image, the processing device120 may determine the first count based on the length of the image anddetermine the second count based on the width of the image. As anotherexample, a relationship between the size of the image, the first count,and the second count may be obtained. The processing device 120 maydetermine the first count and the second count according to therelationship between the size of the image, the first count, and thesecond count. The relationship between the size of the image, the firstcount, and the second count may be set by a user or according to adefault setting of the medical system 100. The processing device 120 mayobtain the relationship between the size of the image, the first count,and the second count from the storage device 130, the storage module412, etc. More descriptions for image block division may be found inFIG. 7 and the descriptions thereof.

In 506, the processing device 120 (e.g., the grayscale featureextraction module 406) may extract grayscale features from each of theplurality of image blocks. As used herein, a grayscale feature extractedfrom an image block may refer to a statistic associated with gray valuesof pixels in the image block. Exemplary statistics associated with grayvalues of pixels in an image block may include a mean, a median, astandard deviation, a variance, etc., of the gray values of the pixelsin the image block.

In some embodiments, the grayscale features may include a grayscalefeature of a first type and a grayscale feature of a second type. Thegrayscale feature of the first type associated with gray values ofpixels in an image block may indicate a level or trend of the grayvalues of the pixels in the image block. The grayscale feature of thesecond type associated with gray values of pixels in an image block mayindicate a deviation between each of the gray values of pixels in theimage block and the grayscale feature of the first type (i.e., the levelor trend of the gray values of the pixels in the image block).

In some embodiments, the grayscale feature of the first type of an imageblock may include a mean or median of gray values of pixels in the imageblock. The grayscale feature of the second type of the image block mayinclude a standard deviation or a variance of the gray values of thepixels in the image block. For example, the grayscale feature of thefirst type of an image block may include a mean of pixel values in theimage block and the grayscale feature of the second type of the imageblock may include a standard deviation of the pixel values in the imageblock.

In some embodiments, if the image represents a subject including onetype of tissues (e.g., adipose tissue), the gray values of pixels ineach image block may be close or similar, and no obvious boundary mayexist in the image. The distribution of the grayscale features of thefirst type in the plurality of image blocks may be relativelyconcentrated, and the grayscale feature of the second type of each imageblock may be relatively smaller with respect to an image that representsone or more subjects including multiple types of tissues (e.g., adiposetissue and bone tissue). If the image represents one or more subjectsincluding multiple types of tissues (e.g., adipose tissue and bonetissue), the gray values of pixels corresponding to different types oftissues may include a large difference, and one or more obviousboundaries may exist in the image. The grayscale feature of the secondtype of an image block may be smaller, and the distribution of thegrayscale features of the first type of image blocks may be relativelydecentralized with respect to an image that represents a subjectincluding one type of tissues (e.g., adipose tissue).

In 508, the processing device 120 (e.g., the segmentation thresholddetermination module 408) may determine a segmentation threshold basedon the grayscale features.

In some embodiments, the processing device 120 may determine arelationship between the grayscale feature of the first type and thegrayscale feature of the second type based on the grayscale featuresextracted from each of the plurality of image blocks. The processingdevice 120 may further the segmentation threshold based on therelationship.

The relationship between the grayscale feature of the first type and thegrayscale feature of the second type may provide a law that thegrayscale feature of the second type changes with the grayscale featureof the first type. In some embodiments, the relationship between thegrayscale feature of the first type and the grayscale feature of thesecond type may be presented in the form of a model, a function, acurve, etc., associated with the grayscale feature of the first type andthe grayscale feature of the second type.

In some embodiments, the processing device 120 may determine, based onthe relationship between the grayscale feature of the first type and thegrayscale feature of the second type, a specific value of the grayscalefeature of the first type when the grayscale feature of the second typeis minimum or maximum locally or globally. The processing device 120 maydesignate the specific value of the grayscale feature of the first typeas the segmentation threshold. For example, if the relationship betweenthe grayscale feature of the first type and the grayscale feature of thesecond type is presented in the form of a curve, the processing device120 may determine the specific value of the grayscale feature of thefirst type corresponding to a peak or a trough of the curve as thesegmentation threshold.

In some embodiments, the processing device 120 may determine, based onthe grayscale features, a plurality of points in a coordinate systemassociated with the grayscale feature of the first type and thegrayscale feature of the second type. Coordinates of each of theplurality of points may represent the grayscale features extracted fromone of the plurality of image blocks. The processing device 120 maydetermine, based on at least a portion of the plurality of points, therelationship between the grayscale feature of the first type and thegrayscale feature of the second type using a fitting technique. Moredescriptions for determining the relationship between the grayscalefeature of the first type and the grayscale feature of the second typemay be found in FIG. 6, and the descriptions thereof.

In 510, the processing device 120 (e.g., the segmentation module 410)may segment the image based on the segmentation threshold.

In some embodiments, the processing device 120 may segment the image bydetermining one or more regions from the image based on the segmentationthreshold. The one or more regions may include a background regionand/or a target region including a representation of at least a portionof the subject. For example, the one or more regions may include thebackground region and/or a breast region.

In some embodiments, the processing device 120 may determine the targetregion by comparing the gray values of the pixels in the image and thesegmentation threshold. For example, if a gray value of a pixel in theimage exceeds the segmentation threshold, the processing device 120 maydetermine that the pixel belongs to the background region; if a grayvalue of a pixel is less than the segmentation threshold, the processingdevice 120 may determine that the pixel belongs to the target region(e.g., the breast region).

In some embodiments, the processing device 120 may further includeperforming an image equalization operation on the image beforesegmenting the image. More descriptions for image equalization may befound in FIGS. 22 and 25, and the descriptions thereof.

In some embodiments, the processing device 120 may further includedetermining one or more characteristic display parameters for displayingthe image based on the determined target region in the image. Moredescriptions for determining one or more characteristic displayparameters may be found in FIGS. 34 and 35, and the descriptionsthereof.

According to some embodiments of the present disclosure, by dividing theimage into multiple image blocks and extracting the grayscale featuresof each of the image blocks, the systems and methods may determine thesegmentation threshold based on the grayscale features of each of theimage block, which may improve the efficiency of segmentation thresholddetermination, thereby improving the efficiency of image segmentation.The segmentation threshold may be correlated with the grayscale featuresof each of the image blocks, which may improve the accuracy of thesegmentation threshold, thereby improving the accuracy of imagesegmentation.

It should be noted that the above description is merely provided forillustration, and not intended to limit the scope of the presentdisclosure. For persons having ordinary skills in the art, multiplevariations and modifications may be made under the teachings of thepresent disclosure. However, those variations and modifications do notdepart from the scope of the present disclosure. In some embodiments,one or more operations may be omitted and/or one or more additionaloperations may be added. For example, operation 502 and operation 504may be combined into a single operation.

FIG. 6 is a flowchart illustrating an exemplary process for determininga segmentation threshold according to some embodiments of the presentdisclosure. In some embodiments, process 600 may be implemented as a setof instructions (e.g., an application) stored in the storage device 130,storage 220, or storage 390. The processing device 120, the processor210, and/or the CPU 340 may execute the set of instructions, and whenexecuting the instructions, the processing device 120, the terminal 140,the processor 210, and/or the CPU 340 may be configured to perform theprocess 600. The operations of the illustrated process presented beloware intended to be illustrative. In some embodiments, the process 600may be accomplished with one or more additional operations not describedand/or without one or more of the operations discussed. Additionally,the order of the operations of process 600 illustrated in FIG. 6 anddescribed below is not intended to be limiting. Operation 508 may beperformed according to process 600 as illustrated in FIG. 6.

In 602, the processing device 120 (e.g., the segmentation thresholddetermination module 408) may determine, based on gray featuresextracted from each of a plurality of image blocks in an image, aplurality of points in a coordinate system associated with the grayfeature of a first type and the gray feature of a second type.

The gray features may be extracted from each of the plurality of imageblocks in the image as described in connection with operation 506 asdescribed in FIG. 5.

Each of the plurality of points may correspond to one of the pluralityof image blocks. In some embodiments, the coordinates of each of theplurality of points may represent the grayscale features for one of theplurality of image blocks. For example, the coordinates of each of theplurality of points may represent the grayscale feature of the firsttype and the grayscale feature of the second type of an image block. Asa further example, the coordinates of each of the plurality of pointsmay represent a mean of gray values of pixels in an image block and astandard deviation of the gray values of the pixels in the image block.

In some embodiments, the coordinate system may include axes (denoted asan X-axis and a Y-axis) representing the grayscale feature of the firsttype and the grayscale feature of the second type. For example, if theX-axis represents the grayscale feature of the first type (e.g., themean of gray values of pixels) and the Y-axis represents the grayscalefeature of the second type (e.g., the standard deviation of gray valuesof pixels), the coordinates of a point may be denoted as (the grayscalefeature of the first type, a grayscale feature of the second type),e.g., (the mean, the standard deviation). If the X-axis represents thegrayscale feature of the second type (e.g., the standard deviation ofgray values of pixels) and the Y-axis represents the grayscale featureof the first type (e.g., the mean of gray values of pixels), thecoordinates of a point may be denoted as (the grayscale feature of thesecond type, the grayscale feature of the first type), e.g., (thestandard deviation, the mean).

In some embodiments, the plurality of points in the coordinate systemmay be distributed in the coordinate system to form a feature scatterplot.

In 604, the processing device 120 (e.g., the segmentation thresholddetermination module 408) may determine at least a portion of theplurality of points by performing at least one of a downsamplingoperation or an operation for removing abnormal points.

In some embodiments, if the size of the image is large, the count of theplurality of image blocks may be large, and the grayscale featuresextracted from the plurality of image blocks may be more, which leads toa large number of the plurality of points in the coordinate system to beprocessed, thereby decreasing a computing speed or efficiency of forsubsequent operations. Therefore, the processing device 120 may performthe downsampling operation on the plurality of points to decrease thecount of points to be processed. By performing a downsampling operation,the processing device 120 may shorten the time-consuming for subsequentoperations, which may improve the efficiency of image segmentation.

In some embodiments, the downsampling operation may be performedaccording to the distance between points among the plurality of points.For example, when the distance between the two points is less than adistance threshold, one of the two points may be retained and the otherone may be removed. As another example, if multiple points are locatedin a region whose area is less than an area threshold, one of themultiple points may be retained and the others may be removed.

In some embodiments, since the image may be affected by various factors(e.g., the positioning of the subject or the scanning dose) during imageacquisition, the image may include one or more pixels (e.g., noises)with abnormal gray values, which may lead to abnormal points in theplurality of points determined based on the grayscale features. A pixelwith an abnormal gray value may also be referred to as an abnormalpixel, such as a noise pixel. Therefore, the processing device 120 mayremove the abnormal points among the plurality of points, which mayimprove the accuracy of a segmentation threshold that is determinedbased on the plurality of points, and improve the robustness of imagesegmentation.

In some embodiments, the processing device 120 may determine a range ofgrayscale features of the first type (e.g., the means corresponding tothe plurality of image blocks) of the plurality of points and divide therange of the grayscale features of the first type into multiplesub-ranges. For each of the multiple sub-ranges, the processing device120 may determine a first mean of grayscale features of the second typeof points each of whose grayscale feature of the first type is in thesub-range. The processing device 120 may classify the points intomultiple groups based on the first mean and determine a second mean ofgrayscale features of the second type of points in each of the multiplegroups. The processing device 120 may determine, based on the secondmean, the abnormal points in each of the multiple groups.

In some embodiments, the processing device 120 may determine the rangeof grayscale features of the first type (e.g., the means correspondingto the plurality of image blocks) of the plurality of points based on amaximum value and a minimum value among the grayscale features of thefirst type (e.g., the means corresponding to the plurality of imageblocks) of the plurality of points. The range of grayscale features ofthe first type may be from the maximum value to the minimum value amongthe grayscale features of the first type (e.g., the means correspondingto the plurality of image blocks) of the plurality of points.

In some embodiments, the count of the multiple sub-ranges may be set bya user or according to a default setting of the medical system 100. Forexample, the processing device 120 may determine the count of themultiple sub-ranges according to the range of the grayscale features ofthe first type. As a further example, the processing device 120 mayobtain a relationship between the range of the grayscale features of thefirst type and the count of the multiple sub-ranges. The processingdevice 120 may determine the count of the multiple sub-ranges based onthe relationship between the range of the grayscale features of thefirst type and the count of the multiple sub-ranges. In someembodiments, the count of the multiple sub-ranges may be 30, or 50, or70, etc.

In some embodiments, the multiple groups may include a first group and asecond group. The first group may include points each of whose grayscalefeature of the second type exceeds the first mean. The second group mayinclude points each of whose grayscale feature of the second type isless than the first mean. The processing device 120 may furtherdetermine the abnormal points based on the second mean and then removethe abnormal points. An abnormal point in the first group may includethe grayscale feature of the second type that exceeds the second mean.An abnormal point in the second group may include the grayscale featureof the second type that is less than the second mean.

Accordingly, the processing device 120 may determine a point whosegrayscale feature of the first type is deviated greatly from the firstmean as an abnormal point. For example, in the first group, thegrayscale feature of the first type of a point whose grayscale featureof the second type exceeds or equals the second mean may deviate greatlyfrom the first mean. In the second group, the grayscale feature of thefirst type of a point whose grayscale feature of the second type is lessthan or equal to the second mean may deviate greatly from the firstmean.

In some embodiments, after the abnormal points are removed based on thesecond mean, the retained points (also referred to as candidate points)may be further classified into multiple groups. The processing device120 may determine a third mean of grayscale features of the second typeof candidate points in each of the multiple groups. The processingdevice 120 may determine abnormal points based on the third mean, whichmay improve the accuracy of abnormal point determination.

In 606, the processing device 120 (e.g., the segmentation thresholddetermination module 408) may determine a relationship between thegrayscale feature of the first type and the grayscale feature of thesecond type based on the at least a portion of the plurality of points.

In some embodiments, based on at least a portion of the plurality ofpoints, the processing device 120 may determine the relationship betweenthe grayscale feature of the first type and the grayscale feature of thesecond type using a fitting technique. Exemplary fitting techniques mayinclude a linear fitting (e.g., a first-order linear fitting, aquadratic linear fitting, a cubic linear fitting, a polynomial curvefitting, etc.), a nonlinear fitting (e.g., the least square fitting),etc.

In some embodiments, the processing device 120 may determine the fittingtechnique based on a count of one or more target regions that need to bedetermined from the image. Each of the one or more target regions mayinclude a representation of at least a portion of a subject (alsoreferred to as a target portion) represented in the image. For example,for a breast image, the one or more regions may include a breast regionincluding a representation of a breast and a pectoral region including arepresentation of at least a portion of a pectoral.

For example, if the count of one or more regions that need to besegmented from the image is large, e.g., exceeding a threshold, acomplex fitting technique (e.g., a cubic linear fitting, a polynomialcurve fitting, etc.) may be used to determine the relationship. As afurther example, if the count of one or more regions that need to besegmented from the image is 2, the fitting technique may include aquadratic fitting; if the count of one or more regions that need to besegmented from the image exceeds 2, the fitting technique may include acomplex fitting (e.g., a cubic linear fitting, a polynomial curvefitting, etc.) than the quadratic curve.

In 608, the processing device 120 (e.g., the segmentation thresholddetermination module 408) may determine a segmentation threshold basedon the relationship between the gray feature of the first type and thegray feature of the second type. For example, the relationship may bedenoted as a fitted curve. The value of the grayscale feature of thefirst type coordinate corresponding to a peak and/or trough of thefitted curve may be used as the gray segmentation threshold. In someembodiments, the value of the grayscale feature of the first typecorresponding to the peak and/or trough point may be not within therange of grayscale features of the first type of the plurality ofpoints, the processing device 120 may change the fitting technique anddetermine the relationship based on the changed fitting technique.

It should be noted that the above description is merely provided forillustration, and not intended to limit the scope of the presentdisclosure. For persons having ordinary skills in the art, multiplevariations and modifications may be made under the teachings of thepresent disclosure. However, those variations and modifications do notdepart from the scope of the present disclosure. In some embodiments,one or more operations may be omitted and/or one or more additionaloperations may be added.

FIG. 7 is a schematic diagram illustrating exemplary image blocks in animage according to some embodiments of the present disclosure. As shownin FIG. 7, the image is a chest medical image. A first count of firstlines along a first direction is 19, and the first lines are c1-c19. Thefirst direction is the vertical direction or column direction. A firstline may also be referred to as a column division line. A second countof second lines along a second direction is 18, and the second lines arer1-r18. The second direction is the horizontal direction or rowdirection. A second line may also be referred to as a row division line.The image is divided into multiple image blocks by the first lines andthe second lines.

FIG. 8 is a schematic diagram illustrating an exemplary fitted curveusing a quadratic fitting technique according to some embodiments of thepresent disclosure. As shown in FIG. 8, the X-axis of a coordinatesystem denotes the mean (i.e., the grayscale feature of the first typeas described in FIG. 6) of gray values of pixels in an image block, andY-axis denotes the standard deviation (i.e., the grayscale feature ofthe second type as described in FIG. 6) of gray values of pixels in animage block. The fitted curve L (i.e., quadratic curve L) is obtained byfitting a plurality of points associated with the grayscale featuresusing the quadratic curve fitting. I₀ is the symmetry axis of the fittedcurve L. The intersection point (X₀) of the I₀ and the X-axis is theabscissa of peak P of the quadratic curve L. And X₀ may be designated asa segmentation threshold.

FIG. 9 is a schematic diagram illustrating an exemplary processingdevice for image segmentation according to some embodiments of thepresent disclosure. As shown, the processing device 120 may include anacquisition module 902, a segmentation threshold determination module904, a segmentation module 906, and a storage module 908. In someembodiments, the acquisition module 902, the segmentation thresholddetermination module 904, the segmentation module 906, and the storagemodule 908 may be connected to and/or communicate with each other via awireless connection (e.g., a network), a wired connection, or acombination thereof.

The acquisition module 902 may be configured to obtain informationrelated to an image. In some embodiments, the image may include amedical image. The image may include a medical image. For example, theimage may include a DR image, an MR image, a PET image, a CT image, etc.As another example, the subject may include a breast. In someembodiments, the acquisition module 902 may obtain the image from thestorage device 130, the storage module 908, a local storage device(e.g., a storage device implemented on the terminal device(s) 140 or themedical device 110) or other storage devices that be in communicationwith the processing device 120. In some embodiments, the acquisitionmodule 902 may obtain the image from an imaging device (e.g., themedical device 110).

In some embodiments, the acquisition module 902 may obtain informationrelated to a preprocessed image that is determined by performing apreprocessing operation on the image. The preprocessing operation mayinclude a filtering operation, a smoothing operation, an enhancementoperation, a suppression operation, a transform operation, or the like,or any combination thereof.

The segmentation threshold determination module 904 may be configured todetermine one or more segmentation threshold. For example, thesegmentation threshold determination module 904 may determine a firstsegmentation threshold based on the image. In some embodiments, thesegmentation threshold determination module 904 may determine the firstsegmentation threshold according to operations a1 to a3. In operationa1, the segmentation threshold determination module 904 may obtain aplurality of candidate segmentation thresholds. In operation a2, thesegmentation threshold determination module 904 may determine multiplesegmentation results based on using each of the plurality candidatesegmentation thresholds based on the image. In operation a3, thesegmentation threshold determination module 904 may determine a firstsegmentation threshold based on the multiple segmentation results.

In some embodiments, the segmentation threshold determination module 904may determine at least a portion of the plurality of candidatesegmentation thresholds based on the gray values of the pixels in theimage. Each of the at least a portion of the plurality of candidatesegmentation thresholds may satisfy a condition. The condition may beset by a user or according to a default setting of the medical system100. In some embodiments, the segmentation threshold determinationmodule 904 may determine the condition based on the gray values and/ordistribution of the gray values in the image.

In some embodiments, the segmentation threshold determination module 904may obtain a segmentation result by classifying the pixels in the image(or the preprocessed image) into multiple groups based on a candidatesegmentation threshold. In some embodiments, the segmentation thresholddetermination module 904 may determine the first segmentation thresholdbased on a portion of process 500. In some embodiments, the segmentationthreshold determination module 904 may determine the first segmentationthreshold using the Otsu algorithm.

In some embodiments, the segmentation threshold determination module 904may divide gray values in the image that exceed the first segmentationthreshold into multiple ranges. For each of the multiple ranges, thesegmentation threshold determination module 904 may determine a count ofpixels in the image whose gray values are in the range. The segmentationthreshold determination module 904 may determine the second segmentationthreshold based on the count of pixels whose gray values are in each ofthe multiple ranges. In some embodiments, the segmentation thresholddetermination module 904 may divide the gray values in the image thatexceed the first segmentation threshold into multiple ranges by dividinga portion of the grayscale histogram of the image with gray valuesexceeding the first segmentation threshold into multiple intervals alongan axis representing gray values.

In some embodiments, the segmentation threshold determination module 904may determine a relationship between a mean of gray values of pixels andthe count of the pixels whose gray values are in the each of themultiple ranges. The segmentation threshold determination module 904 maydetermine the second segmentation threshold based on the relationshipbetween the mean of gray values of pixels and the count of the pixelswhose gray values are in the each of the multiple ranges.

In some embodiments, the segmentation threshold determination module 904may adjust the first segmentation threshold to the trough of thegrayscale histogram by increasing the first segmentation threshold basedon the grayscale histogram of the image when a horizontal coordinate inthe grayscale histogram represents the gray value of each pixel in theimage, and an ordinate coordinate in the grayscale histogram is thecount of pixels with the same gray value.

In some embodiments, the segmentation threshold determination module 904may determine first gray values from an image based on a segmentationthreshold. In some embodiments, the segmentation threshold determinationmodule 904 may determine a second gray value based on the first grayvalues. In some embodiments, the segmentation threshold determinationmodule 904 may determine a target segmentation threshold based on thesegmentation threshold and the second gray value.

The segmentation module 906 may be configured to segment the image basedon the second segmentation threshold.

In some embodiments, the segmentation module 906 may determine abackground region from the image based on the second segmentationthreshold.

In some embodiments, the segmentation module 906 may determine thebackground region of the image based on the second segmentationthreshold and the maximum gray value in the image (or the preprocessedimage).

In some embodiments, the segmentation module 906 may determine firstgray values in the image that exceed the second segmentation threshold,and determine a count of pixels with each of the first gray values. Insome embodiments, the segmentation module 906 may identify a second grayvalue from the first gray values. A count of pixels with the second grayvalue may be maximum among the first gray values. In some embodiments,the segmentation module 906 may determine, based on the secondsegmentation threshold and the second gray value, the background region.

The storage module 908 may be configured to store data, instructions,and/or any other information for an image segmentation. For example, thestorage module 908 may store the image of the subject, segmentationthresholds, etc. In some embodiments, the storage module 908 may be thesame as the storage device 130 in configuration.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. Apparently, for persons having ordinary skills inthe art, multiple variations and modifications may be conducted underthe teachings of the present disclosure. However, those variations andmodifications do not depart from the scope of the present disclosure.For example, the segmentation threshold determination module 904 and thesegmentation module 906 may be integrated into a single module. Asanother example, some other components/modules may be added into and/oromitted from the processing device 120.

FIG. 10 is a flowchart illustrating an exemplary process for imagesegmentation according to some embodiments of the present disclosure. Insome embodiments, process 1000 may be implemented as a set ofinstructions (e.g., an application) stored in the storage device 130,storage 220, or storage 390. The processing device 120, the processor210, and/or the CPU 340 may execute the set of instructions, and whenexecuting the instructions, the processing device 120, the terminal 140,the processor 210, and/or the CPU 340 may be configured to perform theprocess 1000. The operations of the illustrated process presented beloware intended to be illustrative. In some embodiments, the process 1000may be accomplished with one or more additional operations not describedand/or without one or more of the operations discussed. Additionally,the order of the operations of the process 1000 illustrated in FIG. 10and described below is not intended to be limiting.

In 1002, the processing device 120 (e.g., the acquisition module 902)may obtain an image.

In some embodiments, the image may include a medical image. For example,the image may include a DR image, an MR image, a PET image, a CT image,etc. The image may include a representation of a subject. The subjectmay be biological or non-biological. For example, the subject mayinclude a specific portion, organ, and/or tissue of a patient. Asanother example, the subject may include a breast. More descriptions forthe subject may be found elsewhere in the present disclosure (e.g., FIG.1A and the descriptions thereof). The processing device 120 may obtainthe image from the medical device 110, the storage device 130, or anystorage device. More descriptions for the image may be found elsewherein the present disclosure (e.g., FIG. 5, and the descriptions thereof).

In some embodiments, the image may include a plurality of pixels. Eachof the plurality of pixels may be described using a pixel parameter of atype. The type of the pixel parameter may include a grayscale, aluminance, chrominance, etc. Each of the plurality of pixels in theimage may correspond to a value of the pixel parameter of the type. Forexample, if the type of the pixel parameter includes the grayscale, eachof the plurality of pixels in the image may correspond to a value of thegrayscale (also referred to as gray value). If the type of the pixelparameter includes the chrominance, each of the plurality of pixels inthe image may correspond to a value of the chrominance (also referred toas chrominance value). The image may be segmented based on the pixelparameter of the type of each of the plurality of pixels.

The following descriptions are provided with reference to the pixelparameter of the type as the grayscale unless otherwise stated. It isunderstood that this is for illustration purposes and not intended to belimiting. In some embodiments, the image may be in the form of agrayscale histogram. The grayscale histogram may present distribution ofgray values of a plurality of pixels. For example, the grayscalehistogram may present a count of pixels that correspond to the same grayvalue.

In some embodiments, the processing device 120 may perform apreprocessing operation on the image to obtain a preprocessed image. Thepreprocessing operation may include a filtering operation, a smoothingoperation, an enhancement operation, a suppression operation, atransform operation, or the like, or any combination thereof. Theprocessing device 120 may further segment the preprocessed image usingeach of the plurality candidate segmentation thresholds to obtain one ofthe multiple segmentation results.

In some embodiments, the processing device 120 may preprocess the imageby smoothing the grayscale histogram of the image, and/or reduce therange of the grayscale histogram. For example, the maximum gray value inthe grayscale histogram may be BMAX, and the minimum value may be BMIN.In the grayscale histogram, the pixels each of whose gray value exceeds95% of BMAX and is less than 105% of BMIN may be removed from thegrayscale histogram of the image. The smoothing of the grayscalehistogram of the image may increase the efficiency for determining asecond threshold in operation 1006.

In 1004, the processing device 120 (e.g., the segmentation thresholddetermination module 904) may determine a first segmentation thresholdbased on the image.

In some embodiments, the processing device 120 may determine the firstsegmentation threshold according to operations a1 to a3. In operationa1, the processing device 120 may obtain a plurality of candidatesegmentation thresholds. In operation a2, the processing device 120 maydetermine multiple segmentation results based on using each of theplurality candidate segmentation thresholds based on the image. Inoperation a3, the processing device 120 (e.g., the segmentationthreshold determination module 904) may determine a first segmentationthreshold based on the multiple segmentation results.

In some embodiments, the processing device 120 may determine at least aportion of the plurality of candidate segmentation thresholds based onthe gray values of the pixels in the image. Each of the at least aportion of the plurality of candidate segmentation thresholds maysatisfy a condition. The condition may be set by a user or according toa default setting of the medical system 100. In some embodiments, theprocessing device 120 may determine the condition based on the grayvalues and/or distribution of the gray values in the image. For example,the condition may be such that a candidate segmentation threshold isless than a ratio of a total count of the pixels in the image to a totalcount of the gray values in the image (i.e., an average height of thegrayscale histogram of the image). As another example, the condition maybe that a candidate segmentation threshold exceeds a minimum gray valueamong the gray values in the image. As used herein, the total count ofthe gray values in the image refers to a count of gray values afterdeduplication in the image.

For instance, the processing device 120 may designate a portion of thegray values in the image as the at least a portion of the plurality ofcandidate segmentation thresholds. Each of the at least a portion of theplurality of candidate segmentation thresholds may exceed a minimum grayvalue among the gray values in the image and less than an average heightof a grayscale histogram of the image.

Each of the multiple segmentation results may correspond to one of theplurality candidate segmentation thresholds. In some embodiments, theprocessing device 120 may segment the image using each of the pluralitycandidate segmentation thresholds to obtain one of the multiplesegmentation results.

In some embodiments, the processing device 120 may obtain a segmentationresult by classifying the pixels in the image (or the preprocessedimage) into multiple groups based on a candidate segmentation threshold.For example, one of the multiple groups may relate to a first subject(or a tissue of a first type, e.g., adipose tissue) and another one ofthe multiple groups may relate to a second subject (or a tissue of asecond type, e.g., muscle tissue). As another example, one of themultiple groups may relate to a target region (e.g., a breast region) inthe image and another one of the multiple groups may relate to abackground region. As a further example, the processing device 120 mayclassify a pixel whose gray value is less than the candidatesegmentation threshold into one of the multiple groups relating to thetarget region (e.g., a breast region), and classify a pixel whose grayvalue exceeds the candidate segmentation threshold into one of themultiple groups relating to the background region. The processing device120 may determine a variance between means of gray values of pixels inthe multiple groups. The processing device 120 may determine multiplevariances based on the multiple segmentation results. Each of themultiple variances may correspond to one of the multiple segmentationresults generated based on one of the plurality of candidatesegmentation thresholds. The processing device 120 may determine amaximum variance among the multiple variances and designate a candidatesegmentation threshold corresponding to the maximum variance as thefirst segmentation threshold.

In some embodiments, the processing device 120 may determine the firstsegmentation threshold based on a portion of process 500. For example,the processing device 120 may determine a plurality of image blocks inthe image and extract grayscale features from each of the plurality ofimage blocks. The processing device 120 may determine the firstsegmentation threshold based on the grayscale features extracted fromeach of the plurality of image blocks. More descriptions for determiningthe first segmentation threshold may be found in FIG. 5 and FIG. 6.

In some embodiments, the processing device 120 may determine the firstsegmentation threshold using the Otsu algorithm. The Otsu algorithm mayalso be referred to as the maximum between-class variance algorithm,which is an adaptive threshold determination technique. Using the Otsualgorithm, the processing device 120 may divide the pixels in the imageinto a first group (e.g., a background group) and a second group (e.g.,a target group.) using each of a plurality of candidate segmentationthresholds. The processing device 120 may determine a variance between amean of gray values of pixels in the first group and a mean of grayvalues of pixels in the second group. The processing device 120 mayfurther determine the first segmentation threshold based on thevariance.

In 1006, the processing device 120 (e.g., the segmentation thresholddetermination module 904) may determine a second segmentation thresholdbased on the first segmentation threshold and gray values of pixels inthe image.

In some embodiments, the processing device 120 may divide gray values inthe image that exceed the first segmentation threshold into multipleranges. For each of the multiple ranges, the processing device 120 maydetermine a count of pixels in the image whose gray values are in therange. The processing device 120 may determine the second segmentationthreshold based on the count of pixels whose gray values are in each ofthe multiple ranges. In some embodiments, the processing device 120 maydivide the gray values in the image that exceed the first segmentationthreshold into multiple ranges by dividing a portion of the grayscalehistogram of the image with gray values exceeding the first segmentationthreshold into multiple intervals along an axis representing grayvalues.

In some embodiments, the processing device 120 may determine arelationship between a mean of gray values of pixels and the count ofthe pixels whose gray values are in the each of the multiple ranges. Forexample, the processing device 120 may determine a fitting curve basedon the mean of gray values of pixels and the count of pixels whose grayvalues are in the each of the multiple ranges. The processing device 120may determine a specific range from the multiple ranges where a troughof the fitting curve belongs to. The processing device 120 may determinea median or the mean of gray values in the specific range as the secondsegmentation threshold.

The processing device 120 may adjust the first segmentation threshold tothe trough of the grayscale histogram by increasing the firstsegmentation threshold based on the grayscale histogram of the imagewhen a horizontal coordinate in the grayscale histogram represents thegray value of each pixel in the image, and an ordinate coordinate in thegrayscale histogram is the count of pixels with the same gray value. Thecount of pixels corresponding to the trough of the grayscale histogrammay be less than the count of pixels with other gray values around thetrough. The gray value corresponding to the trough of the grayscalehistogram may be designated as the second segmentation threshold.Accordingly, multiple pixels that are classified into the backgroundregion based on the first segmentation threshold may be classified intoa target region based on the second segmentation threshold, which mayimprove the accuracy for image segmentation.

In 1008, the processing device 120 (e.g., the segmentation module 906)may segment the image based on the second segmentation threshold.

In some embodiments, the processing device 120 may determine abackground region from the image based on the second segmentationthreshold. For example, the processing device 120 may classify pixelswhose gray values exceed the second segmentation threshold to thebackground region.

In some embodiments, the processing device 120 may determine thebackground region of the image based on the second segmentationthreshold and the maximum gray value in the image (or the preprocessedimage). For example, the background region may include pixels whose grayvalues in the range from the second segmentation threshold to themaximum gray value in the image (or the preprocessed image).

As another example, the processing device 120 may decrease the maximumgray value with a proportion to obtain a decreased maximum gray value.The background region may include pixels whose gray values are in therange from the second segmentation threshold to the decreased maximumgray value. The proportion may be set by a user, or according to adefault setting of the medical system 100. For example, the proportionmay be 10%, 5%, or other values. The decreased maximum gray value may be90% of the maximum gray value, 95% of the maximum gray value, 98% of themaximum gray value, etc.

In some embodiments, the processing device 120 may determine first grayvalues in the image that exceed the second segmentation threshold, anddetermine a count of pixels with each of the first gray values. Theprocessing device 120 may identify a second gray value from the firstgray values. A count of pixels with the second gray value may be maximumamong the first gray values. The processing device 120 may determine,based on the second segmentation threshold and the second gray value,the background region. More descriptions for determining the backgroundregion based on the second segmentation threshold may be found in FIG.11.

It should be noted that the above description is merely provided forillustration, and not intended to limit the scope of the presentdisclosure. For persons having ordinary skills in the art, multiplevariations and modifications may be made under the teachings of thepresent disclosure. However, those variations and modifications do notdepart from the scope of the present disclosure. In some embodiments,one or more operations may be omitted and/or one or more additionaloperations may be added.

FIG. 11 is a flowchart illustrating an exemplary process for determininga background region from the image according to some embodiments of thepresent disclosure. In some embodiments, process 1100 may be implementedas a set of instructions (e.g., an application) stored in the storagedevice 130, storage 220, or storage 390. The processing device 120, theprocessor 210, and/or the CPU 340 may execute the set of instructions,and when executing the instructions, the processing device 120, theterminal 140, the processor 210, and/or the CPU 340 may be configured toperform the process 1100. The operations of the illustrated processpresented below are intended to be illustrative. In some embodiments,the process 1100 may be accomplished with one or more additionaloperations not described and/or without one or more of the operationsdiscussed. Additionally, the order of the operations of the process 1100illustrated in FIG. 11 and described below is not intended to belimiting. Operation 1008 in FIG. 10 may be performed according toprocess 1100 as illustrated in FIG. 11.

In 1102, the processing device 120 (e.g., the segmentation thresholddetermination module 904) may determine first gray values from an imagebased on a segmentation threshold.

The segmentation threshold may be determined as described elsewhere inthe present disclosure (e.g., FIG. 10, and the descriptions thereof).The segmentation threshold may also be referred to as a candidatesegmentation threshold. For example, the processing device 120 maydesignate the first segmentation threshold determined as described inoperation 1004 in FIG. 10 as the segmentation threshold. As anotherexample, the processing device 120 may designate the second segmentationthreshold as described in 1006 in FIG. 10 as the segmentation threshold.As still another example, the processing device 120 may determine thesegmentation threshold based on a portion of processing 500 as describedin FIG. 5.

In some embodiments, the processing device 120 may determine first grayvalues in the image that exceed the segmentation threshold. For example,FIG. 12 shows an exemplary grayscale histogram of the image according tosome embodiments of the present disclosure. As shown in FIG. 12, thegrayscale histogram of the image includes a plurality of points. Avertical coordinate of a point denotes a gray value in the image, and ahorizontal coordinate of the point denotes the count of pixels with thegray value. X2 refers to the segmentation threshold. X4 refers to themaximum gray value in the image. The processing device 120 may designategray values in the image that are in a range from X2 to X4 as the firstgray values. The pixels with the first gray values may belong to anestimated background region that is segmented based on the segmentationthreshold.

In 1104, the processing device 120 (e.g., the segmentation thresholddetermination module 904) may determine a second gray value based on thefirst gray values.

In some embodiments, the processing device 120 may determine a count ofpixels of each of the first gray values in the image and identify thesecond gray value from the first gray values based on the count ofpixels of each of the first gray values. A count of pixels with thesecond gray value may be maximum among the first gray values. As shownin FIG. 12, X3 denotes the second gray value that is a gray value of apoint whose vertical coordinate is maximum among points located at theright side of the segmentation threshold X2 on the grayscale histogram(i.e., points whose horizontal coordinates exceed the segmentationthreshold X2). In other words, the second gray value corresponds to apeak of a portion of the grayscale histogram that is located at theright side of the segmentation threshold X2. In some embodiments, theprocessing device 120 may obtain the point with the largest verticalcoordinate (that is, the largest count of pixels) by comparing point bypoint in the portion of the grayscale histogram that is located at theright side of the segmentation threshold. In some embodiments, theprocessing device 120 may sort all points in the grayscale histogramthat is located the right of the segmentation threshold (e.g., thesecond segmentation threshold as described in FIG. 10) according to theordinates of the all points to obtain the point with the largestvertical coordinate.

In some embodiments, the processing device 120 may classify gray valuesin the image that exceed the segmentation threshold (e.g., the secondsegmentation threshold as described in FIG. 10) into multiple ranges.The processing device 120 may determine an average count of pixels whosegray values are in each of the multiple ranges. The processing device120 may determine one of the multiple ranges with the largest averagecount of pixels. The processing device 120 may determine a mean or amedian of gray values of pixels whose gray values are in the one of themultiple ranges with the largest average count of pixels as the secondgray value.

In 1106, the processing device 120 (e.g., the segmentation thresholddetermination module 904) may determine a target segmentation thresholdbased on the segmentation threshold and the second gray value. In someembodiments, the target segmentation threshold may also be referred toas a third segmentation threshold.

In some embodiments, the processing device 120 may determine whether theimage includes a background region based on the segmentation threshold(e.g., the second segmentation threshold as described in FIG. 10) andthe second gray value. For example, the processing device 120 maydetermine a count of pixels with the segmentation threshold and a countof pixels with the second gray value in the image. The processing device120 may determine whether the image includes the background region basedon a difference between the count of pixels with the segmentationthreshold (e.g., the second segmentation threshold as described in FIG.10) and the count of pixels with the second gray value. In response to adetermination that the difference between the count of pixels with thesegmentation threshold (e.g., the second segmentation threshold asdescribed in FIG. 10) and the count of pixels with the second gray valueis less than a count threshold, the processing device 120 may determinethat the background region does not exists in the image, i.e., the grayvalues in the image may be stable. In response to a determination thatthe difference between the count of pixels with the segmentationthreshold and the count of pixels with the second gray value exceeds thecount threshold, the processing device 120 may determine that thebackground region exists in the image. The count threshold may be set bya user, or may be set according to an experience value, and may be adefault value, such as 20, 25, 30, or a larger or smaller value.

In some embodiments, the processing device 120 may determine whether theimage include the background according to process 1400 as illustrated inFIG. 14.

In some embodiments, if the image includes the background region, theprocessing device 120 may determine the target segmentation thresholdbased on the segmentation threshold and the second gray value.

In some embodiments, the processing device 120 may determine a thirdcount of pixels with a same gray value based on a first count of pixelswith a same gray value that is equal to the second segmentationthreshold and a second count of pixels with a same gray value that isequal to the second gray value in the image. The processing device 120may further determine the target segmentation threshold based on thethird count of pixels with the same gray value. For example, theprocessing device 120 may determine, based on the third count of pixelswith the same gray value, a third gray value in the image. The thirdgray value may exceed the second segmentation threshold and is less thanthe second gray value. A count of pixels with the third gray value inthe image may be equal to the third count of pixels. The processingdevice 120 may designate the third gray value as the target segmentationthreshold. As another example, the processing device 120 may identify apoint from the grayscale histogram of the image whose verticalcoordinate is equal to the third count of pixels and designate the grayvalue of the point as the target segmentation threshold. The targetsegmentation threshold may also be referred to as a backgroundsegmentation threshold.

In some embodiments, the processing device 120 may determine the thirdcount of pixels by averaging the first count of pixels and the secondcount of pixels. In some embodiments, the processing device 120 maydetermine a difference between the first count of pixels and the secondcount of pixels. The processing device 120 may determine the third countof pixels based on the first count of pixels and the second count ofpixels and a difference between the first count of pixels and the secondcount of pixels. For example, the processing device 120 may decrease thedifference between the first count of pixels and the second count ofpixels with a proportion (e.g., 92%, 90%, 85%) to obtain a decreaseddifference. The decreased difference may be 8%, 10%, 25%, etc., of thedifference between the first count of pixels and the second count ofpixels. The processing device 120 may determine a sum between the firstcount of pixels and the decreased difference or a sum of the secondcount of pixels and the decreased difference as the third count ofpixels.

In some embodiments, the processing device 120 may determine multipledifferent third gray values each of which corresponds to the third countof pixels. The processing device 120 may determine a mean, a median, amaximum gray value, or a minimum gray value, etc., of the multipledifferent third gray values as the target segmentation threshold.

In some embodiments, the processing device 120 may determine a point ina portion of the grayscale histogram of the image between thesegmentation threshold and the second gray value that includes a maximumchange rate of counts of pixels. The processing device 120 may designatethe gray value of the point as the target segmentation threshold.

In 1108, the processing device 120 (e.g., the segmentation module 906)may determine a background region from the image based on the targetsegmentation threshold.

In some embodiments, the background region may include pixels each ofwhich gray value exceeds the target segmentation threshold.

Accordingly, the systems and methods of the present disclosure maydetermine the target segmentation threshold for determining a backgroundregion based on a difference between the first segmentation thresholdand the second segmentation threshold, which may improve the accuracy ofthe target segmentation threshold. By determining whether the imageincludes the background region, thus the false detection of thebackground region may be effectively decreased, and the accuracy ofbackground region detection may be further improved.

It should be noted that the above description is merely provided forillustration, and not intended to limit the scope of the presentdisclosure. For persons having ordinary skills in the art, multiplevariations and modifications may be made under the teachings of thepresent disclosure. However, those variations and modifications do notdepart from the scope of the present disclosure. In some embodiments,one or more operations may be omitted and/or one or more additionaloperations may be added.

FIG. 13 is a schematic diagram illustrating an exemplary processingdevice for determining image type according to some embodiments of thepresent disclosure. As shown, the processing device 120 may include anacquisition module 1302, an image block determination module 1304, astatistic result determination module 1306, and an image typedetermination module 1308. In some embodiments, the acquisition module1302, the image block determination module 1304, the statistic resultdetermination module 1306, and the image type determination module 1308may be connected to and/or communicate with each other via a wirelessconnection (e.g., a network), a wired connection, or a combinationthereof.

The acquisition module 1302 may be configured to obtain informationrelated to a first image of a subject.

In some embodiments, the first image may be acquired by an imagingdevice (e.g., the medical device 110). In some embodiments, the firstimage may be obtained by performing a preprocessing operation on animage of the subject acquired by the imaging device.

The imaging device may include a camera, a video camera, a medicalimaging device, an industrial X-ray flaw detection device, a securitymonitoring device, etc. The medical imaging device (e.g., the medicaldevice 110) may include a CT device, a DR device, a mammography device,a CR device, a DSA device, a mobile X-ray device (such as a mobile C-armmachine), a screen X-ray device, an MRI device, an ultrasound device, aSPECT, a PET, a γ camera, etc. For example, the first image may be amedical image acquired by the DR or the mammography device. In someembodiments, the first image may be a 2D image or a 3D image. Moredescriptions of the subject may be found elsewhere in the presentdisclosure.

In some embodiments, the acquisition module 1302 may obtain the firstimage from the imaging device, for example, via a wired or wirelessconnection (e.g., the network 150). In some embodiments, the acquisitionmodule 1302 may obtain the image from the storage device 130, a localstorage device (e.g., a storage device implemented on the terminaldevice(s) 140 or the medical device 110, or the processing device 120,etc.) or other storage devices that be in communication with theprocessing device 120.

In some embodiments, the acquisition module 1302 may obtain a secondimage of the first image. The second image may be an operation image ofthe first image.

The image block determination module 1304 may be configured to determineone or more image blocks in an image. For example, the image blockdetermination module 1304 may be configured to determine one or morefirst image blocks in the first image. As another example, the imageblock determination module 1304 may determine one or more second imageblocks in the second image.

In some embodiments, the image block determination module 1304 maydivide the first image to obtain the one or more first image blocks. Thedivision of the first image may be a regular division or an irregulardivision. As used herein, the regular division refers to dividing animage according to a rule. The rule may relate to the size of each imageblock, the shape of each image block, a position of each image block,etc. The determination of the second image blocks may be the same as orsimilar to the determination of the one or more first image blocks.

The statistic result determination module 1306 may be configured todetermine one or more statistic result associated with a pixel parameterof a type for pixels in each of one or more image blocks. The pixelparameter of a pixel may indicate the attribute of the pixel. The typeof the pixel parameter may include grayscale, chrominance, luminance (orbrightness), etc. The attribute of the pixel may be indicated by a valueof the pixel parameter (also referred to as a pixel value, such as agray value) and a type of the pixel parameter. For example, thestatistic result determination module 1306 may be configured todetermine a first statistic result associated with a pixel parameter ofa type for pixels in each of the one or more first image blocks. Asanother example, the statistic result determination module 1306 may beconfigured to determine a second statistic result associated with apixel parameter of a type for pixels in each of the one or more secondimage blocks.

In some embodiments, the statistic result determination module 1306 mayobtain the first statistic result corresponding to a first image blockusing a calculation formula of each of the one or more statistic values,such as a variance calculation formula, a mean calculation formula, astandard deviation calculation formula, or the like, or a combinationthereof.

In some embodiments, the statistic result determination module 1306 maydetermine a grayscale histogram of pixels in a first image block andthen determine the statistic values based on the grayscale histogram.

The determination of the second statistic result may be the same as orsimilar to the determination of the first statistic result.

The image type determination module 1308 may be configured to determinea type of the first image based on the first statistic result and/or thesecond statistic result. The type of the first image may indicatewhether the first image includes a region representing air.

In some embodiments, the image type determination module 1308 maydetermine the type of the first image based on the type of each of atleast a portion of the one or more first image blocks.

In some embodiments, the image type determination module 1308 maydetermine the type of a first image block by comparing the firststatistic result with a first condition to obtain a first comparisonresult. The image type determination module 1308 may determine the typeof a first image block based on the first comparison result.

In some embodiments, the image type determination module 1308 may obtainthe first comparison result by comparing the one or more statisticvalues of a first image block with the one or more first statisticthresholds, respectively. The image type determination module 1308 maydetermine the type of the first image block based on the firstcomparison result.

In some embodiments, the image type determination module 1308 maydetermine the type of the first image using an image classificationmodel based at least in part on the first statistic result. The imageclassification model may include a statistical model, a trained machinelearning model, or the like. The statistical model may include amultiple regression model, a cluster analysis model, a discriminantanalysis model, a principal component analysis model, a factor analysismodel, etc. The trained machine learning model may be constructed baseda logistic regression model, a k-nearest neighbor (kNN) model, a naiveBayes (NB) model, a support vector machine (SVM), a decision tree (DT)model, a random forests (RF) model, a classification and regressiontrees (CART) model, a gradient boosting decision tree (GBDT) model, axgboost model (eXtreme Gradient Boosting), an Adaboost model, a lightgradient boosting machine (Light GBM), a gradient boosting machine(GBM), a least absolute shrinkage and selection operator (LASSO), anartificial neural network (ANN) model, etc. The image classificationmodel may be obtained by a processing device that is the same as ordifferent from the processing device 120 using a plurality of trainingsamples based on a training algorithm (e.g., a backpropagationalgorithm, a gradient descent algorithm, etc.). Each of the plurality oftraining samples may include a statistic result (e.g., one or morestatistical values) and the corresponding image type of an image blockor an image. The statistic result may be used as an input and thecorresponding image type may be used as a desired output in a trainingprocess of a machine learning model.

In some embodiments, the image type determination module 1308 may inputthe first statistic result of the one or more first image blocks intothe image classification model to obtain the type of the first image. Insome embodiments, the image type determination module 1308 may input thefirst statistic result of each of the one or more first image blocksinto the image classification model to obtain the type of the each ofthe one or more first image blocks.

In some embodiments, the image type determination module 1308 maydetermine the type of a first image block by comparing the firststatistic result with a first condition to obtain a first comparisonresult and comparing the second statistic result to obtain a secondcomparison result. The image type determination module 1308 maydetermine the type of a first image block based on the first comparisonresult and the second comparison result.

In some embodiments, the image type determination module 1308 maydetermine the type of a first image block by comparing the secondstatistic result to obtain a second comparison result. The image typedetermination module 1308 may determine the type of a first image blockbased on the second comparison result.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. Apparently, for persons having ordinary skills inthe art, multiple variations and modifications may be conducted underthe teachings of the present disclosure. However, those variations andmodifications do not depart from the scope of the present disclosure.For example, some other components/modules (e.g., a storage module) maybe added into and/or omitted from the processing device 120.

FIG. 14 is a flowchart illustrating an exemplary process for determiningimage type according to some embodiments of the present disclosure. Insome embodiments, process 1400 may be implemented as a set ofinstructions (e.g., an application) stored in the storage device 130,storage 220, or storage 390. The processing device 120, the processor210 and/or the CPU 340 may execute the set of instructions, and whenexecuting the instructions, the processing device 120, the terminal 140,the processor 210 and/or the CPU 340 may be configured to perform theprocess 1400. The operations of the illustrated process presented beloware intended to be illustrative. In some embodiments, the process 1400may be accomplished with one or more additional operations not describedand/or without one or more of the operations discussed. Additionally,the order of the operations of the process 1400 illustrated in FIG. 14and described below is not intended to be limiting.

In 1402, the processing device 120 (e.g., the acquisition module 1302)may obtain a first image of a subject.

In some embodiments, the first image may be acquired by an imagingdevice (e.g., the medical device 110). In some embodiments, the firstimage may be obtained by performing a preprocessing operation on animage of the subject acquired by the imaging device. For example, theprocessing device 120 may perform a negative film operation on the imageof the subject. More descriptions for the preprocessing operation may befound elsewhere in the present disclosure (e.g., FIG. 10 and thedescriptions thereof).

The imaging device may include a camera, a video camera, a medicalimaging device, an industrial X-ray flaw detection device, a securitymonitoring device, etc. The medical imaging device (e.g., the medicaldevice 110) may include a CT device, a DR device, a mammography device,a CR device, a DSA device, a mobile X-ray device (such as a mobile C-armmachine), a screen X-ray device, an MRI device, an ultrasound device, aSPECT, a PET, a y camera, etc. For example, the first image may be amedical image acquired by the DR or the mammography device. In someembodiments, the first image may be a 2D image or a 3D image. Moredescriptions of the subject may be found elsewhere in the presentdisclosure.

In some embodiments, the processing device 120 may obtain the firstimage from the imaging device, for example, via a wired or wirelessconnection (e.g., the network 150). In some embodiments, the processingdevice 120 may obtain the image from the storage device 130, a localstorage device (e.g., a storage device implemented on the terminaldevice(s) 140 or the medical device 110, or the processing device 120,etc.) or other storage devices that be in communication with theprocessing device 120.

In 1404, the processing device 120 (e.g., the image block determinationmodule 1304) may determine one or more first image blocks in the firstimage.

In some embodiments, the processing device 120 may divide the firstimage to obtain the one or more first image blocks. The division of thefirst image may be a regular division or an irregular division. As usedherein, the regular division refers to dividing an image according to arule. The rule may relate to the size of each image block, the shape ofeach image block, a position of each image block, etc. For example, theregular division of an image may include dividing the image intomultiple image blocks whose sizes and/or shapes are the same. Theirregular division of an image may include dividing the image intomultiple image blocks whose sizes and/or shapes are different. Asanother example, the regular division of an image may include dividingthe image into multiple image blocks whose positions in the image aredistributed along a row direction and/or a column direction of theimage. The irregular division of an image may include dividing the imageinto multiple image blocks whose positions in the image are distributeddisorderly. In some embodiments, as the number (or count) of rows is notan integral multiple of the number (or count) of rows of an image blockand/or the number (or count) of columns of the image is not an integralmultiple of columns (or count) of the image block obtained by theregular division, the sizes of image blocks located at an edge region ofthe image may be different from the sizes of image blocks located atother regions such as the middle region of the image. This is alsowithin the scope of the present disclosure.

In some embodiments, adjacent first image blocks may not include anoverlapped region. For example, the processing device 120 may determinea first count of first lines along a first direction and a second countof second lines along a second direction. The processing device 120 maydetermine the one or more first image blocks based on the first count offirst lines and the second count of second lines. A first image blockmay be defined by at least one first line and at least one second line.As shown in FIG. 7, adjacent image blocks do not include an overlappedregion.

In some embodiments, adjacent first image blocks may include anoverlapped region. For example, the processing device 120 maysequentially obtain the one or more first image blocks of a×a size by astep size b from the first image. When the step size b is less than a,adjacent first image blocks may include an overlapped region.

In 1406, the processing device 120 (e.g., the statistic resultdetermination module 1306) may determine a first statistic resultassociated with a pixel parameter of a type for pixels in each of theone or more first image blocks.

The pixel parameter of a pixel may indicate the attribute of the pixel.The type of the pixel parameter may include grayscale, chrominance,luminance (or brightness), etc. The attribute of the pixel may beindicated by a value of the pixel parameter (also referred to as a pixelvalue, such as a gray value) and a type of the pixel parameter.

Each of the one or more first image blocks may correspond to a firststatistic result. The first statistic result corresponding to a firstimage block may include one or more statistic values associated withvalues of the pixel parameter of the type of the pixels in the firstimage block. The one or more statistic values may include a sum, avariance, a mean, a standard deviation of values of the pixel parameterof the type of pixels in the first image block.

In some embodiments, the processing device 120 may obtain the firststatistic result corresponding to a first image block using acalculation formula of each of the one or more statistic values, such asa variance calculation formula, a mean calculation formula, a standarddeviation calculation formula, or the like, or a combination thereof.

In some embodiments, the processing device 120 may determine a grayscalehistogram of pixels in a first image block and then determine thestatistic values based on the grayscale histogram.

In 1408, the processing device 120 (e.g., the image type determinationmodule 1308) may determine a type of the first image based at least inpart on the first statistic result.

The type of the first image may indicate whether the first imageincludes a region representing air. For example, the type of the firstimage may include that the first image includes a region representingair (or referred to as an air region for brevity), that the first imagelacks or does not include any air region, or that the first imageincludes one or more intermediate regions. As used herein, an air regionin an image refers to a portion of the image representing a physicalregion that includes only air and is void of any imagable item. As usedherein, an imagable item refers to an item including at least onenon-air material. The non-air material may be biological ornon-biological including, e.g., water, blood, bone, soft tissue, hardtissue, plastic, metal. For instance, an imagable item may be a soliditem including at least one non-air material, a porous item including atleast one non-air material, or an item with a 2D or 3D boundary made ofa non-air material and/or within which there is at least one non-airmaterial. For example, an air region in an image may include abackground region. The first image lacking or not including an airregion may also refers to an image void of an air region. Anintermediate region in an image refers to a region in the image that isother than an air region and also other than a region void of air. Insome embodiments, an intermediate region in an image refers to a portionof the image representing a physical region with some air and at leastan imagable item, in which the proportion of the volume of air to thetotal volume of the physical region is below a proportion threshold.

For example, FIG. 19 and FIG. 20 show exemplary images of differenttypes according to some embodiments of the present disclosure. FIG. 19shows an image of a breast obtained after a negative film operation wasperformed on a breast image acquired by a mammography device. FIG. 20shows an image of the chest obtained after a negative film operation wasperformed on a chest image acquired by a DR device. As shown in FIG. 19,region 1910 is an air region (i.e., background region), and region 1920is a region representing airless representing a breast (i.e., breastregion). As shown in FIG. 20, region 2010 is a region representing air,region 2020 is a region void of air that includes a representation ofthe chest cavity, and region 2030 is an intermediate region, thatincludes a representation of cloth and air. According to someembodiments of the present disclosure, the types of regions and/orimages may effectively and quickly be distinguished, which may improveimage processing speed.

In some embodiments, the processing device 120 may determine the type ofthe first image based on the type of each of at least a portion of theone or more first image blocks. For example, if one of the one or morefirst image blocks include an air region, the type of the first imagemay indicate that the first image includes an air. If each of the firstimage blocks includes a region void of air, the type of the first imagemay indicate that the first image lacks or does not include any airregion. If one of the one or more first image blocks includes anintermediate region, the type of the first image may indicate that thefirst image includes an intermediate region.

In some embodiments, the processing device 120 may determine the type ofa first image block by comparing the first statistic result with a firstcondition to obtain a first comparison result. The processing device 120may determine the type of a first image block based on the firstcomparison result.

In some embodiments, the first condition may include one or more firststatistic thresholds corresponding to the one or more statistic valuesin the first statistic result. The first statistic thresholds may be setby a user or according to a default setting of the medical system 100.In some embodiments, the first statistic thresholds may be stored in astorage device (e.g., the storage device 130). The processing device 120may obtain the first statistic thresholds from the storage device. Insome embodiments, the first statistic thresholds may be determined basedon prior statistical knowledge. The first statistic thresholds may beadjusted according to different application scenarios, which is notspecifically limited in this disclosure.

In some embodiments, the processing device 120 may obtain the firstcomparison result by comparing the one or more statistic values of afirst image block with the one or more first statistic thresholds,respectively. The processing device 120 may determine the type of thefirst image block based on the first comparison result. For example, ifthe first statistical result includes a first mean and a first standarddeviation of gray values of pixels in a first image block, the firstcondition may include a first mean threshold and a first standarddeviation threshold. The processing device 120 may compare the firstmean with the first mean threshold and compare the first standarddeviation with the first standard deviation threshold to obtain thefirst comparison result. In some embodiments, the first comparisonresult may include a magnitude relationship between each statistic valueand its corresponding threshold. For example, the first comparisonresult may be that the first mean exceeds the first mean threshold, andthe first standard deviation exceeds the first standard deviationthreshold. After obtaining the first comparison result, the type of thefirst image block may be obtained. For example, if the first comparisonresult of a first image block includes that the first mean exceeds thefirst mean threshold and the first standard deviation exceeds the firststandard deviation threshold, the processing device 120 may determinethat the first image block includes an air region. If the processingdevice 120 determines that one of the one or more first image blocksincludes an air region, the processing device 120 may determine the typeof the first image indicating that the first image includes an airregion.

If the first comparison result of a first image block includes that thefirst mean is less than the first mean threshold and the first standarddeviation is less than the first standard deviation threshold, theprocessing device 120 may determine that the first image block includesa region void of air. If the processing device 120 determines that eachof the one or more first image blocks includes a region void of air, theprocessing device 120 may determine the type of the first imageindicating that the first image does not include or lack any air region.

If the first comparison result of a first image block includes that thefirst mean exceeds or equals the first mean threshold and the firststandard deviation is less than or equal to the first standard deviationthreshold, or the first comparison result of a first image blockincludes that the first mean is less than or equal to the first meanthreshold and the first standard deviation exceeds or equals the firststandard deviation threshold, the processing device 120 may determinethat the first image block include an intermediate region. Theprocessing device 120 may further determine the type of the first imageindicating that the first image includes an intermediate region.

In some embodiments, the processing device 120 may determine the type ofthe first image using an image classification model based at least inpart on the first statistic result. The image classification model mayinclude a statistical model, a trained machine learning model, or thelike. The statistical model may include a multiple regression model, acluster analysis model, a discriminant analysis model, a principalcomponent analysis model, a factor analysis model, etc. The trainedmachine learning model may be constructed based a logistic regressionmodel, a k-nearest neighbor (kNN) model, a naive Bayes (NB) model, asupport vector machine (SVM), a decision tree (DT) model, a randomforests (RF) model, a classification and regression trees (CART) model,a gradient boosting decision tree (GBDT) model, a xgboost model (eXtremeGradient Boosting), an Adaboost model, a light gradient boosting machine(Light GBM), a gradient boosting machine (GBM), a least absoluteshrinkage and selection operator (LASSO), an artificial neural network(ANN) model, etc. The image classification model may be obtained by aprocessing device that is the same as or different from the processingdevice 120 using a plurality of training samples based on a trainingalgorithm (e.g., a backpropagation algorithm, a gradient descentalgorithm, etc.). Each of the plurality of training samples may includea statistic result (e.g., one or more statistical values) and thecorresponding image type of an image block or an image. The statisticresult may be used as an input and the corresponding image type may beused as a desired output in a training process of a machine learningmodel.

In some embodiments, the processing device 120 may input the firststatistic result of the one or more first image blocks into the imageclassification model to obtain the type of the first image. In someembodiments, the processing device 120 may input the first statisticresult of each of the one or more first image blocks into the imageclassification model to obtain the type of the each of the one or morefirst image blocks.

Accordingly, the systems and methods of the present disclosure maydetermine the type of an image based on the type of image blocks in animage and difference information in the image, which may improve theefficiency and accuracy of image classification. By determining whetherthe image includes air region with improved accuracy, thus the falsedetection of a background region may be effectively decreased, and theaccuracy of background region detection may be further improved.

It should be noted that the above description is merely provided forillustration, and not intended to limit the scope of the presentdisclosure. For persons having ordinary skills in the art, multiplevariations and modifications may be made under the teachings of thepresent disclosure. However, those variations and modifications do notdepart from the scope of the present disclosure. In some embodiments,one or more operations may be omitted and/or one or more additionaloperations may be added.

FIG. 15 is an exemplary flowchart illustrating an exemplary process fordetermining image type according to some embodiments of the presentdisclosure. In some embodiments, process 1500 may be implemented as aset of instructions (e.g., an application) stored in the storage device130, storage 220, or storage 390. The processing device 120, theprocessor 210 and/or the CPU 340 may execute the set of instructions,and when executing the instructions, the processing device 120, theterminal 140, the processor 210 and/or the CPU 340 may be configured toperform the process 1500. The operations of the illustrated processpresented below are intended to be illustrative. In some embodiments,the process 1500 may be accomplished with one or more additionaloperations not described and/or without one or more of the operationsdiscussed. Additionally, the order of the operations of the process 1500illustrated in FIG. 15 and described below is not intended to belimiting.

In 1502, the processing device 120 (e.g., the image type determinationmodule 1308) may determine a second image based on a first image.

The first image may be obtained as described in connection withoperation 1402 as described in FIG. 14.

In some embodiments, the second image may include difference informationof the first image. The difference information may refer to differencesin an attribute of pixels caused by the pixels located at differentlocations or regions in the first image. For example, the differencebetween the gray value of a pixel with a high gray value (for example,200) and a gray value with a low gray value (for example, 10) mayindicate that the two pixels are in different locations of the image. Apixel value (e.g., the gray value or the brightness value) of a secondpixel in the second image may show a difference between first pixelsthat are associated with the second pixel and are at different locationsof the first image. As used herein, first pixels in the first imageassociated with a second pixel in a second image may refer to that avalue of a pixel parameter of the second pixel is determined based onvalues of the pixel parameter of the first pixels in the first image.

In some embodiments, the second image may be obtained by performing oneor more algebraic operations (e.g., an add operation, a subtractionoperation, a multiplication operation, a division operation, a squareoperation, square, an integral operation, and/or a differentialoperation, etc.) on values of a pixel parameter (such as pixel values,gray values, or brightness values, etc.) of corresponding pixels in thefirst image. The second image may also be referred to as an operationimage of the first image. As used herein, two or more correspondingpixels in the same image may refer to that the two or more pixels are intwo or more columns and the same row, or the two or more pixels are intwo or more rows and the same column.

In some embodiments, the one or more algebraic operations may beperformed between each two or more pixels that are in two or morecolumns and the same row of the first image. For example, the processingdevice 120 may replace a gray value of each pixel in a specific columnof the first image with the difference between the gray value of eachpixel in the specific column and a gray value of the corresponding pixelthat is in a column adjacent to or not the specific column and the samerow as the gray value of the each pixel in the specific column.

In some embodiments, the one or more algebraic operations may beperformed between two or more pixels that are in two or more rows andthe same column of the first image. For example, the processing device120 may replace a gray value of each pixel in a specific row of thefirst image with the difference between the gray value of each pixel inthe specific row and a gray value of the corresponding pixel that is ina row adjacent to or not to the specific row and the same column as thegray value of each pixel in the specific row.

In some embodiments, the second image may be obtained by compositingmultiple operation images of the first image. For example, theprocessing device 120 may perform a first algebraic operation betweeneach two or more pixels that are in two or more columns and in the samerow of the first image to obtain a first operation image. The processingdevice 120 may perform a second algebraic operation between each two ormore pixels that are in two or more rows and in the same column of thefirst image to obtain a second operation image. The first operationimage and the second operation image may also be referred to as thirdimages. The processing device 120 may determine the second image bycompositing the first operation image and the second operation image.For example, the processing device 120 may perform an add operationbetween corresponding pixels in the first operation image and the secondoperation image. As used herein, two or more corresponding pixels indifferent images may refer to that the two or more pixels are located inthe same column and same row of the different images.

The first algebraic operation and the second algebraic operation may bethe same or different. For example, the first algebraic operation mayinclude an add operation and the second algebraic operation may includea subtraction operation. As another example, the first algebraicoperation and the second algebraic operation may be both the subtractionoperation.

For example, the processing device 120 may obtain the first operationimage by replacing a gray value of each pixel in a specific column ofthe first image with the difference between the gray value of each pixelin the specific row and a gray value of the corresponding pixel that isin a column adjacent to or not to the specific column and the same rowas the gray value of each pixel in the specific column The processingdevice 120 may obtain the second operation image by replacing a grayvalue of each pixel in a specific row of the first image with thedifference between the gray value of each pixel in the specific row anda gray value of the corresponding pixel that is in a row adjacent to ornot to the specific row and the same column as the each pixel in thespecific row. The processing device 120 may determine the second imageby performing an add operation between two corresponding pixels that arein the same row and column of the first operation image and the secondoperation image.

In some embodiments, the first operation image may include a count ofcolumns that are different from a count of columns in the secondoperation image and/or the first operation image may include a count ofrows that are different from a count of rows in the second operationimage. For example, the first operation image may include the count ofcolumns less than the count of columns in the second operation image.The first operation image may include the count of rows exceeding thecount of rows in the second operation image. The processing device 120may determine a first portion of pixels values in the second image byperforming an add operation between two corresponding pixels that are inthe same row and column of the first operation image and the secondoperation image. The processing device 120 may determine a secondportion of pixels values in the second image by designating pixel valuesin one or more columns and/or rows in the first operation image or thesecond operation image as the second portion of pixels values in thesecond image.

For example, if the count of columns in the first operation image isless than the count of columns in the second operation image, theprocessing device 120 may designate pixel values in one or more columnsof the second operation image as pixel values in corresponding columnsof the second image; if the count of rows in the first operation imageexceeds the count of rows in the second operation image, the processingdevice 120 may designate pixel values in the one or more rows of thefirst operation image as pixel values of corresponding rows of thesecond image.

More descriptions for the algebraic operations and operation images maybe found elsewhere in the present disclosure (e.g., FIGS. 17A-17C andFIGS. 18A-18B, and the descriptions thereof).

In 1504, the processing device 120 (e.g., the image block determinationmodule 1304) may determine one or more second image blocks in the secondimage.

In some embodiments, the count of the second image blocks may be thesame as the count of the first image blocks. Each of the one or moresecond image blocks may correspond to one of the one or more first imageblocks. The size and shape of a second image block may be the same as ordifferent from the size and shape of a first image block thatcorresponds to the second image block, respectively. As used herein, asecond image block corresponding to a first image block may refer tothat two corresponding image blocks in the first image and the secondimage may represent a same portion or position of the subject or the twocorresponding image blocks are located in the same position in the firstimage and the second image.

In some embodiments, the count of the second image blocks may bedifferent from the count of the first image blocks. For example, thecount of the second image blocks may be less than the count of the firstimage blocks. Each of the one or more second image blocks may correspondto one or more of the one or more first image blocks. The size and/orshape of at least one second image block may be different from the sizeand/or shape of a first image block, respectively.

In some embodiments, the size of the first image and the size of thesecond image may be different, the size of a first image block obtainedby the same division mode as a second image block at the edge of the twoimages (i.e., the first image and the second image) may be different.

The one or more second image blocks may be divided from the second imageas similar to or same as the division of the one or more first imageblocks in the first image. More descriptions for image blockdetermination may be found in FIG. 14. For example, the processingdevice 120 may divide the second image into the one or more second imageblocks of a×a size. As another example, the processing device 120 maydetermine a first count of lines along a first direction and determine asecond count of lines along a second direction. The processing device120 may divide the second image into the one or more second image blocksbased on the first count of first lines and the second count of secondlines. In some embodiments, the first direction may be perpendicular tothe second direction. As still another example, the processing device120 may sequentially obtain the one or more second image blocks of a×asize by a step size b (e.g., b<a) such that two adjacent second imageblocks may have an overlapped region.

In 1506, the processing device 120 (e.g., the image type determinationmodule 1308) may determine a second statistic result associated with thepixel parameter of the type for pixels in each of the one or more secondimage blocks.

Each of the one or more second image blocks may correspond to a secondstatistic result. The second statistic result corresponding to a secondimage block may include one or more statistic values on values of thepixel parameter of the type of the pixels in the second image block. Theone or more statistic values may include a sum, a variance, a mean, astandard deviation, etc., of values of the pixel parameter of the typeof pixels in the second image block. The determination of the secondstatistic result may be similar to or the same as the determination ofthe first statistic result.

In 1508, the processing device 120 (e.g., the image type determinationmodule 1308) may determine the type of the first image based on a firststatistic result associated with the first image and the secondstatistic result.

In some embodiments, the type of the first image may indicate whetherthe first image includes a region representing air. For example, thetype of the first image may include that the first image includes aregion representing air, the first image does not include a regionrepresenting air, or the first image includes a region representing fewair. More descriptions for the type of the first image may be foundelsewhere in the present disclosure (e.g., FIG. 14 and the descriptionsthereof).

The first statistic result may be determined as described in connectionwith operation 1406 as described in FIG. 14. Each of the one or morefirst image blocks may correspond to a first statistic result. Each ofthe one or more first image blocks may correspond to one or more secondimage blocks. The first statistic result corresponding to a first imagemay correspond to one or more second statistic results corresponding toone or more second image blocks.

In some embodiments, the processing device 120 may determine the type ofthe first image by determining the types of the one or more first imageblocks. The type of a first image block may indicate that the firstimage block includes a region representing air, or includes a regionrepresenting airless, or includes a region representing few airless. Forexample, if one of the one or more first image blocks includes a regionrepresenting air, the processing device 120 may determine the type ofthe first image indicating that the first image includes a regionrepresenting air; if each of the one or more first image blocks includesa region representing airless, the processing device 120 may determinethe type of the first image indicating that the first image does notinclude a region representing air; if one of the one or more first imageblocks includes a region representing few air, the processing device 120may determine the type of the first image indicating that the firstimage includes a region representing few air.

In some embodiments, the processing device 120 may determine whether thefirst statistic result satisfies a first condition and the secondstatistic satisfies a second condition. The processing device 120 mayfurther determine the type of a first image block based on whether thefirst statistic result of the first image block satisfies the firstcondition and whether the second statistic result of a second imageblock corresponding to the first image block satisfies the secondcondition. In response to determination that the first statistic resultsatisfies the first condition and the second statistic result satisfiesthe second condition, the processing device 120 may determine that thefirst image block includes a region representing air. In response todetermination that the first statistic result satisfies the firstcondition and the second statistic result does not satisfy the secondcondition or the first statistic result does not satisfy the firstcondition and the second statistic result satisfies the secondcondition, the processing device 120 may determine that the first imageblock includes a region representing few air. In response todetermination that the first statistic result does not satisfy the firstcondition and the second statistic result does not satisfy the secondcondition, the processing device 120 may determine that the first imageblock includes a region representing airless.

In some embodiments, the processing device 120 may compare the firststatistic result with a first condition to obtain a first comparisonresult and compare the second statistic with a second condition toobtain a first comparison result. The processing device 120 maydetermine the type of a first image block based on the first comparisonresult and the second comparison result. In some embodiments, the firstcondition may include one or more first statistic thresholds each ofwhich corresponds to one of one or more first statistic values in thefirst statistic result. The second condition may include one or moresecond statistic thresholds each of which corresponds to one of one ormore second statistic values in the second statistic result. The firststatistic thresholds and/or the second statistic thresholds may be setby a user or according to a default setting of the medical system 100.In some embodiments, the first statistic thresholds and/or the secondstatistic thresholds may be stored in a storage device (e.g., thestorage device 130). The processing device 120 may obtain the firststatistic thresholds and/or the second statistic thresholds from thestorage device. In some embodiments, the first statistic thresholdsand/or the second statistic thresholds may be determined based on priorstatistical knowledge. The first statistic thresholds and/or the secondstatistic thresholds may be adjusted according to different applicationscenarios, which is not specifically limited in this disclosure.

In some embodiments, if each of the first statistic values of the secondimage block is less than a corresponding first statistic threshold of afirst image block and each of the second statistic values of a secondimage block corresponding to the first image block exceeds acorresponding second statistic threshold, the processing device 120 maydetermine the type of the first image block indicating that the firstimage block includes a region void of air; if each of the firststatistic values of the second image block exceeds a corresponding firststatistic threshold of a first image block and each of the secondstatistic values of a second image block corresponding to the firstimage block is less than a corresponding second statistic threshold, theprocessing device 120 may determine the type of the first image blockindicating that the first image block includes an air region.

For example, if the one or more first statistic values of the firstimage block includes a first mean value of gray values of pixels in thefirst image block, and the one or more second statistic values of asecond image block corresponding to the first image block includes asecond mean and a second standard deviation of gray values of the pixelsin the second image block, the first condition may include a first meanthreshold corresponding to the one or more first statistic values (i.e.,the first mean) of the first image block, and the second condition mayinclude a second mean threshold and a second standard deviationthreshold corresponding to the statistics values (i.e., the second meanand the second standard deviation) of the second image block.

If the second mean of the second image block exceeds the second meanthreshold, the second standard deviation of the second image blockexceeds the second standard deviation threshold, and the first mean ofthe corresponding first image block is less than the first meanthreshold, the processing device 120 may determine the type of the firstimage block indicating that the first image block includes a region voidof air; if the second mean value of the second image block is less thanthe second mean threshold value, the second standard deviation of thesecond image block is less than the second standard deviation threshold,and the first second mean value of the corresponding first image blockexceeds the second mean threshold, the processing device 120 maydetermine the type of the first image block indicating that the firstimage block includes an air region; if the second mean value of thesecond image block is less than the second mean threshold value, thesecond standard deviation of the second image block is less than thesecond standard deviation threshold, and the first second mean value ofthe corresponding first image block is less than the second meanthreshold, or the second mean value of the second image block exceedsthe second mean threshold value, the second standard deviation of thesecond image block exceeds the second standard deviation threshold, andthe first second mean value of the corresponding first image blockexceeds the second mean threshold, the processing device 120 maydetermine the type of the first image block indicating that the firstimage block includes an intermediate region.

In some embodiments, the processing device 120 may determine the type ofthe first image using an image classification model based on the firststatistic result and the second statistic result. For example, theprocessing device 120 may input the first statistic result, and thesecond statistic result into the image classification model and obtainthe type of the first image outputted by the image classification model.The image classification model may be obtained by a processing devicethat is the same as or different from the processing device 120 using aplurality of training samples based on a training algorithm (e.g., abackpropagation algorithm, a gradient descent algorithm, etc.). Each ofthe plurality of training samples may include a first statistic result,a second statistic result (e.g., one or more statistical values), andthe corresponding image type of an image block or an image. Moredescriptions for the image classification model may be found elsewherein the present disclosure (e.g., FIG. 14, and the descriptions thereof).

It should be noted that the above description is merely provided forillustration, and not intended to limit the scope of the presentdisclosure. For persons having ordinary skills in the art, multiplevariations and modifications may be made under the teachings of thepresent disclosure. However, those variations and modifications do notdepart from the scope of the present disclosure. In some embodiments,one or more operations may be omitted and/or one or more additionaloperations may be added.

FIG. 16 is a flowchart for an exemplary process for determining imagetype according to some embodiments of the present disclosure. In someembodiments, process 1600 may be implemented as a set of instructions(e.g., an application) stored in the storage device 130, storage 220, orstorage 390. The processing device 120, the processor 210 and/or the CPU340 may execute the set of instructions, and when executing theinstructions, the processing device 120, the terminal 140, the processor210 and/or the CPU 340 may be configured to perform the process 1600.The operations of the illustrated process presented below are intendedto be illustrative. In some embodiments, the process 1600 may beaccomplished with one or more additional operations not described and/orwithout one or more of the operations discussed. Additionally, the orderof the operations of the process 1600 illustrated in FIG. 16 anddescribed below is not intended to be limiting.

In 1602, the processing device 120 (e.g., the acquisition module 1302)may obtain a first image.

In some embodiments, the first image may be obtained as described inconnection with operation 1402 as described in FIG. 14.

In 1604, the processing device 120 (e.g., the acquisition module 1302)may obtain a second image based on the first image.

In some embodiments, the second image may include difference informationof the first image. The second image may be obtained as described inconnection with operation 1502 as described in FIG. 15.

In 1606, the processing device 120 (e.g., the image block determinationmodule 1304) may determine one or more second image blocks in the secondimage.

The one or more second image blocks in the second image may correspondto one of one or more first image blocks in the first image. In someembodiments, the one or more second image blocks in the second image maybe determined as described in connection with operation 1504 asdescribed in FIG. 15.

In 1608, the processing device 120 (e.g., the image type determinationmodule 1308) may determine at least one statistic result associated witha pixel parameter of a type for pixels in each of the one or more secondimage blocks.

In some embodiments, the processing device 120 may determine at leastone statistic result associated with a pixel parameter of a type forpixels in each of the one or more second image blocks. The at least onestatistic result may also be referred to as the second statistic resultas described elsewhere in the present disclosure. More descriptions fordetermining the second statistic result may be found in FIG. 15 and thedescriptions thereof.

In 1610, the processing device 120 (e.g., the image type determinationmodule 1308) may determine a type of the first image based at least inpart on the at least one statistic result.

The type of the first image may indicate whether the first imageincludes a region representing air. For example, the type of the firstimage may include that the first image includes a region representingair (or referred to as an air region for brevity), that the first imagelacks or does not include any air region, or that the first imageincludes one or more intermediate regions. For example, the type of thefirst image may include that the first image includes a regionrepresenting air, the first image does not include a region representingair, or the first image includes one or more other regions. Moredescriptions for the type of the first image may be found elsewhere inthe present disclosure (e.g., FIG. 14 and the descriptions thereof).

In some embodiments, the processing device 120 may determine the type ofthe first image based on the type of each of the one or more secondimage blocks. For example, if one of the one or more second image blocksincludes an air region, the type of the first image may indicate thatthe first image includes an air region. If all regions corresponding tothe second image blocks are void of air, the first image may not includeor lack any air region. If one of the one or more second image blocks,the first image may include an intermediate region, the type of thefirst image may indicate that the first image includes an intermediateregion.

In some embodiments, the processing device 120 (e.g., the image typedetermination module 1308) may determine the type of the first image bycomparing the at least one statistic result with a condition. Thecondition may include one or more statistic thresholds corresponding tothe one or more statistic values in the statistic result. The statisticthresholds may be set by a user or according to a default setting of themedical system 100. In some embodiments, the statistic thresholds may bestored in a storage device (e.g., the storage device 130). Theprocessing device 120 may obtain the second statistic thresholds fromthe storage device. In some embodiments, the statistic thresholds may bedetermined based on prior statistical knowledge. The statisticthresholds may be adjusted according to different application scenarios,which is not specifically limited in this disclosure.

In some embodiments, the processing device 120 may compare the statisticresult of a second image block with the one or more statistic thresholdsto obtain a comparison result. The processing device 120 may determinethe type of the second image block based on the comparison result. Forexample, if the statistical result includes a mean and a standarddeviation of gray values of pixels in a second image block, thecondition may include a mean threshold and a standard deviationthreshold. The processing device 120 may compare the mean with the meanthreshold and compare the standard deviation with the standard deviationthreshold to obtain the comparison result. In some embodiments, thecomparison result may include a magnitude relationship between eachstatistic value and its corresponding threshold. For example, thecomparison result may be that the mean exceeds the mean threshold, andthe standard deviation exceeds the standard deviation threshold. Afterobtaining the comparison result, the type of the second image block maybe obtained. For example, if the comparison result of a second imageblock is that the mean exceeds the mean threshold and the standarddeviation exceeds the standard deviation threshold, the processingdevice 120 may determine that the second image block in the second imageincludes a region void of air. If the processing device 120 determinesthat a region corresponding to each of the one or more second imageblocks in the second image includes a region void of air, the processingdevice 120 may determine that the type of the first image indicatingthat the first image does not or lack any air region.

If the comparison result of a second image block is that the mean isless than the mean threshold and the s standard deviation is less thanthe second standard deviation threshold, the processing device 120 maydetermine that the second image block includes an air region. Theprocessing device 120 may determine the type of the first imageindicating that the first image includes an air region.

If the comparison result of a second image block is that the meanexceeds or equals the mean threshold and the standard deviation is lessthan or equal to the standard deviation threshold, or the comparisonresult of a second image block is that the mean is less than or equal tothe mean threshold and the standard deviation exceeds or equals thestandard deviation threshold, the processing device 120 may determinethat the second image block includes an intermediate region. Theprocessing device 120 may further determine the type of the first imageindicating that the first image includes an intermediate region.

In some embodiments, the processing device 120 may determine the type ofthe first image using an image classification model based on thestatistic result. The image classification model may be obtained by aprocessing device that is the same as or different from the processingdevice 120 using a plurality of training samples based on a trainingalgorithm (e.g., a backpropagation algorithm, a gradient descentalgorithm, etc.). Each of the plurality of training samples may includea statistic result (e.g., one or more statistical values) and thecorresponding image type of an image block or an image.

FIG. 17A is a schematic diagram illustrating an exemplary first imageaccording to some embodiments of the present disclosure. As shown inFIG. 1A7, the first image includes a size of 3×3. The position of eachpixel in the first image may be denoted by a row (denoted by a number p)and a column (denoted by number q) where the pixel belongs to. Forexample, the position of a pixel in the first row and the first columnis denoted as 1/1. The value of a pixel parameter (e.g., pixel value,grayscale, or brightness) of each pixel may be denoted by a letter, forexample, A, B, C, etc.

An operation image of the first image as shown in FIG. 17A may beobtained by performing an algebraic operation on pixel values ofcorresponding pixels that are in multiple rows and the same column ofthe first image. For example, FIG. 17B shows a first operation image ofthe first image as shown in FIG. 17A according to some embodiments ofthe present disclosure. As shown in FIG. 17B, a subtraction operationwas performed between pixel values of corresponding pixels in twoadjacent rows of the first image to obtain the first operation image inFIG. 17B. Pixel values of pixels in the first row of the first operationimage were determined by subtracting pixel values of correspondingpixels in the second row of the first image from pixel values ofcorresponding pixels in the first row of the first image. Especially,the pixel value of pixel 1/1 in the first operation image in FIG. 17B isequal to a difference between pixel values of pixel 1/1 and pixel 2/1,i.e., A-D. The pixel value of pixel 1/2 is equal to a difference betweenpixel values of pixel 1/2 and pixel 2/2, i.e., B-E. The pixel value ofpixel 1/3 is equal to a difference between pixel values of pixel 1/3 andpixel 2/3, i.e., C-F. Pixel values of the pixels in the second row ofthe first operation image were determined by subtracting pixel values ofcorresponding pixels in the third row of the first image from pixelvalues of corresponding pixels in the second row of the first image. Thepixel values of pixels 2/1, 2/2, and 2/3 in the first operation image inFIG. 17B is equal to D-G, E-H, and F-I, respectively.

For the pixels in the third row of the first image, since the third rowis the last row of the first image, the subtraction operation may be notperformed on the third row. After the subtraction operation wasperformed, the first operation image with a size of 2×3 was obtained asshown in FIG. 17B.

In some embodiments, the first operation image as shown in FIG. 17B mayinclude a third row. The pixel value of each of pixels in the third rowof the first operation image may be equal to a pixel value of acorresponding pixel in the third row of the first image.

As another example, FIG. 17C shows a second operation image of the firstimage as shown in FIG. 17A according to some embodiments of the presentdisclosure. As shown in FIG. 17C, a subtraction operation was performedbetween pixel values of corresponding pixels in two adjacent columns ofthe first image to obtain the second operation image in FIG. 17B.

Pixel values of the pixels in the first column of the operation imagewere determined by subtracting pixel values of corresponding pixels inthe second column of the first image from pixel values of correspondingpixels in the first column of the first image. Especially, the pixelvalue of pixel 1/1 in the second operation image in FIG. 17C is equal toa difference between pixel values of pixel 1/1 and pixel 1/2, i.e., A-B.The pixel value of pixel 2/1 is equal to a difference between pixelvalues of pixel 1/1 and pixel 2/2, i.e., D-E. The pixel value of pixel3/1 is equal to a difference between pixel values of pixel 3/1 and pixel3/2 i.e., G-H.

Pixel values of the pixels in the second column of the second operationimage were determined by subtracting pixel values of correspondingpixels in the third column of the first image from pixel values ofcorresponding pixels in the second column of the first image. The pixelvalues of pixels 1/2, 2/2, and 3/2 in the second operation image in FIG.1A7B is equal to D-G, E-H, and F-I, respectively.

For the pixels in the third column of the first image, since the thirdcolumn is the last column of the first image, the subtraction operationmay be not performed on the third column. After the subtractionoperation was performed, the second operation image with a size of 3×2was obtained as shown in FIG. 17C.

In some embodiments, the second operation image shown in FIG. 17C mayinclude a third column. The pixel value of each of pixels in the thirdcolumn may be equal to the pixel value of a corresponding pixel in thethird column of the first image.

In some embodiments, the processing device 120 may perform one or morealgebraic operations on the pixel values of corresponding pixels in thefirst operation image and the second operation image to obtain one ormore third operation images. For example, FIG. 18A-18B show third imagesobtained based on the first operation image and the second operationimage as shown in FIG. 17B and FIG. 17C according to some embodiments ofthe present disclosure. An add operation is performed between pixelvalues of corresponding pixels in the first operation image and thesecond operation image.

In a third operation image as shown in FIG. 18A, the pixel value ofpixel 1/1 is equal to (|A−B|+|A−D|), the pixel value of pixel 1/2 isequal to (|B−E|+|B−C|), the pixel value of pixel 2/1 is equal to(|D−G|+|D−E|), and the pixel value of pixel 2/2 is equal to(|E−H|+|E−F|).

The first operation image and the second operation image are indifferent sizes. The size of a third image may be determined based onthe sizes of the first operation image and the second operation image.For example, the length of the first operation image is less than thewidth of the first image. The length of the second first operation imageexceeds the width of the first image. The third image may include thelength the same as the width. As shown in FIG. 18A, the width of thethird operation image is equal to the width of the first operation imageand the length of the third image is equal to the length of the secondoperation image. As shown in FIG. 18B, the width of the third operationimage is equal to the width of the second operation image and the lengthof the third image is equal to the length of the first operation image.

As the width of the first operation image exceeds the width of thesecond operation image, each of pixels in the third column in the firstoperation image may not include a corresponding pixel in the secondoperation image when performing the add operation. The pixels in thethird row of the third operation image in FIG. 18A may be determinedbased on the pixel values of the pixels in the third row of the firstoperation image and/or the pixel values of the pixels in the third rowof the first image. For example, the pixel values of the pixels in thethird column of the first operation image may be designated as pixelvalues of pixels in the third column of the third operation image inFIG. 18A. Especially, the pixel value of pixel 1/3 is equal to (|C−F|),the pixel value of pixel 2/3 is equal to (|F−I|), and the pixel value ofpixel 3/3 is equal to I.

As the length of the second operation image exceeds the length of thefirst operation image, each of pixels in the third row in the secondoperation image may not include a corresponding pixel in the firstoperation image when performing the add operation. The pixels in thethird row of the third operation image in FIG. 18A may be determinedbased on pixel values of the pixels in the third row of the firstoperation image and/or pixel values of pixels in the third row of thefirst image. For example, the pixel value of pixel 3/1 is equal to(|G−H|), the pixel value of pixel 3/2 is equal to |H−I|, and the pixelvalue of pixel 3/3 is equal to I.

As shown in FIG. 18B, pixels in the third column of the first operationimage were removed and pixels in the third row of the second operationimage to obtain the same size of the first operation image and thesecond operation image. The third image was obtained by performing anadd operation between pixel values of corresponding pixels in the firstoperation image were removed and the second operation image with thesame size.

It should be noted that the above description is merely provided forillustration, and not intended to limit the scope of the presentdisclosure. For persons having ordinary skills in the art, multiplevariations and modifications may be made under the teachings of thepresent disclosure. However, those variations and modifications do notdepart from the scope of the present disclosure. In some embodiments,one or more operations may be omitted and/or one or more additionaloperations may be added.

FIG. 21 is a schematic diagram illustrating an exemplary processingdevice for processing image according to some embodiments of the presentdisclosure. As shown, the processing device 120 may include a firstacquisition module 2102, an image generation module 2104, a transformmodel determination module 2106, and a storage module 2108. In someembodiments, the first acquisition module 2102, the image generationmodule 2104, the transform model determination module 2106, and thestorage module 2108 may be connected to and/or communicate with eachother via a wireless connection (e.g., a network), a wired connection,or a combination thereof.

The first acquisition module 2102 may be configured to obtain an imageof a subject. In some embodiments, the image may include a medicalimage. For example, the image may include a DR image, an MR image, a PETimage, a CT image, a DBT device, etc. The subject may be biological ornon-biological. For example, the subject may include a patient, aman-made object, etc. As another example, the subject may include aspecific portion (e.g., the chest, the abdomen, the head, the waist,etc.), organ, and/or tissue of a patient. More descriptions for thesubject may be found elsewhere in the present disclosure (e.g., FIG. 1Aand the descriptions thereof).

In some embodiments, the first acquisition module 2102 may obtain theimage from the storage device 130, a local storage device (e.g., astorage device implemented on the terminal device(s) 140) or otherstorage devices that be in communication with the processing device 120.In some embodiments, the first acquisition module 2102 may obtain theimage from an imaging device (e.g., the medical device 110). The imagingdevice may include a digital X radiographic device (e.g., a DR device, aCT device, etc.).

The image may include image information, such as a signal to noiseratio, gray values, a contrast, a resolution, etc.

The image generation module 2104 may be configured to generate one ormore images.

In some embodiments, the image generation module 2104 may generate afirst image and a second image based on the image of the subject. Insome embodiments, the first image may represent low-frequencyinformation in the image, and the second image may representhigh-frequency information in the image. The low-frequency informationin the image may also be referred to as a low-frequency component orlow-frequency signal.

In some embodiments, the image generation module 2104 may obtain thefirst image using a low-pass filter to filter the image. The image mayinclude gray values of pixels denoted by f(i,j). The first image mayinclude gray values denoted by f_(L)(i,j), and (i,j) represent theposition of each pixel in the image or the first image.

In some embodiments, the image generation module 2104 may obtain thesecond image by subtracting the gray value f_(L)(i,j) of a pixel in thefirst image from the gray value f(i,j) of a corresponding pixel in theimage.

In some embodiments, the image generation module 2104 may obtain thegray values f_(H)(i,j) of the second image by filtering the image usinga high-pass filter. In some embodiments, the image generation module2104 may obtain the first image by subtracting the gray value f_(H)(i,j)of each pixel of the second image from the gray value f(i,j) of acorresponding pixel in the image.

In some embodiments, the image generation module 2104 may determine athird image by processing the first image using a local transform model.

In some embodiments, the image generation module 2104 may input the grayvalues of the pixels in the first image into the local transform model.

In some embodiments, the image generation module 2104 may adjust grayvalues of pixels in each of a portion of the multiple ranges using acorresponding transform sub-model to obtain the third image.

In some embodiments, the image generation module 2104 may determine atarget image of the subject based on the third image and the secondimage.

In some embodiments, the image generation module 2104 may determine thetarget image by fusing the third image (also referred to as a firstlow-frequency image) and the second image.

In some embodiments, the image generation module 2104 may transform thesecond image (i.e., the high-frequency image) to obtain a fourth image(also referred to as a first high-frequency image). In some embodiments,the image generation module 2104 may determine the target image byfusing the third image and the fourth image.

In some embodiments, the image generation module 2104 may transform thethird image using a global transform model to obtain a fifth image (alsoreferred to as a transformed third image or second low-frequency image).In some embodiments, the image generation module 2104 may determine thetarget image based on the fifth image and the second image.

In some embodiments, the image generation module 2104 may transform thesecond image (i.e., the high-frequency image) based on a transform ratiobetween corresponding pixels in the fifth image and the first image toobtain a sixth image (also referred to as second high-frequency image).In some embodiments, the image generation module 2104 may determine thetarget image by fusing the fifth image and the sixth image.

The transform model determination module 2106 may be configured todetermine one or more transform models. In some embodiments, a transformmodel may be configured to equalize an image. For example, the transformmodel determination module 2106 may determine a local transform model.In some embodiments, the transform model determination module 2106 maydetermine multiple ranges of gray values represented in the first image.For example, the first image may include a target region and abackground region. The target region in the first image may include arepresentation of at least a portion of the subject. The backgroundregion may include a representation of air. The image generation module2104 may determine the multiple ranges of gray values based on asegmentation threshold between the target region and the backgroundregion. The transform model determination module 2106 may determine thelocal transform model based at least in part on the multiple ranges ofthe gray values of the pixels in the first image.

In some embodiments, the transform model determination module 2106 maydetermine multiple transform sub-models each of which corresponds to oneof the multiple ranges based at least in part on the corresponding rangeof gray values. In some embodiments, the transform model determinationmodule 2106 may determine the local transform local based on themultiple transform sub-models.

In some embodiments, the transform model determination module 2106 maydetermine the local transform model based on multiple local equalizationintensities each of which corresponds to one of the multiple ranges.

In some embodiments, the transform model determination module 2106 maydetermine a global transform model.

In some embodiments, the transform model determination module 2106 maydetermine transform ratios each of which corresponds to twocorresponding pixels in the transformed third image and the first image,based on a gray value of each of pixels in the fifth image and a grayvalue of each of pixels in the first image.

The storage module 2108 may be configured to store data, instructions,and/or any other information for image equalization. For example, thestorage module 2108 may store the image of the subject, transformmodels, etc. In some embodiments, the storage module 2108 may be thesame as the storage device 130 in configuration.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. Apparently, for persons having ordinary skills inthe art, multiple variations and modifications may be conducted underthe teachings of the present disclosure. However, those variations andmodifications do not depart from the scope of the present disclosure.For example, some other components/modules may be added into and/oromitted from the processing device 120.

FIG. 22 is a flowchart illustrating an exemplary process for imageequalization according to some embodiments of the present disclosure. Insome embodiments, process 2200 may be implemented as a set ofinstructions (e.g., an application) stored in the storage device 130,storage 220, or storage 390. The processing device 120, the processor210, and/or the CPU 340 may execute the set of instructions, and whenexecuting the instructions, the processing device 120, the terminal 140,the processor 210 and/or the CPU 340 may be configured to perform theprocess 2200. The operations of the illustrated process presented beloware intended to be illustrative. In some embodiments, the process 2200may be accomplished with one or more additional operations not describedand/or without one or more of the operations discussed. Additionally,the order of the operations of the process 2200 illustrated in FIG. 22and described below is not intended to be limiting.

In 2202, the processing device 120 (e.g., the first acquisition module2102) may obtain an image of a subject.

In some embodiments, the image may include a medical image. For example,the image may include a DR image, an MR image, a PET image, a CT image,a DBT device, etc. The subject may be biological or non-biological. Forexample, the subject may include a patient, a man-made object, etc. Asanother example, the subject may include a specific portion (e.g., thechest, the abdomen, the head, the waist, etc.), organ, and/or tissue ofa patient. More descriptions for the subject may be found elsewhere inthe present disclosure (e.g., FIG. 1A and the descriptions thereof).

In some embodiments, the processing device 120 may obtain the image fromthe storage device 130, a local storage device (e.g., a storage deviceimplemented on the terminal device(s) 140) or other storage devices thatbe in communication with the processing device 120. In some embodiments,the processing device 120 may obtain the image from an imaging device(e.g., the medical device 110). The imaging device may include a digitalX radiographic device (e.g., a DR device, a CT device, etc.).

The image may include image information, such as a signal to noiseratio, gray values, a contrast, a resolution, etc.

In 2204, the processing device 120 (e.g., the image generation module2104) may determine a first image and a second image based on the imageof the subject.

In some embodiments, the first image may represent low-frequencyinformation in the image, and the second image may representhigh-frequency information in the image. The low-frequency informationin the image may also be referred to as a low-frequency component orlow-frequency signal. The low-frequency information in the image mayrelate to pixels in a region whose pixel values change smoothly. Thefirst image may also be referred to as a low-frequency image. Thehigh-frequency information in the image may also be referred to as ahigh-frequency component or high-frequency signal. The high-frequencyinformation in the image may relate to pixels in a region (e.g., noises,edges, etc.) whose pixel values change dramatically. The second imagemay also be referred to as a high-frequency image.

In some embodiments, the processing device 120 may obtain the firstimage using a low-pass filter to filter the image. The image may includegray values of pixels denoted by f(i,j). The first image may includegray values denoted by f_(L)(i,j), and (i,j) represent the position ofeach pixel in the image or the first image. In some embodiments, thelow-pass filter may include a mean filter, a bilateral filter, aGaussian filter, a median filter, a universal filter, a separablefilter, or the like.

In some embodiments, the processing device 120 may obtain the secondimage by subtracting the gray value f_(L)(i,j) of a pixel in the firstimage from the gray value f(i,j) of a corresponding pixel in the image.The second image may include gray values denoted by f_(H)(i,j), and(i,j) represent the position of each pixel in the second first image.

In some embodiments, the processing device 120 may obtain the grayvalues f_(H)(i,j) of the second image by filtering the image using ahigh-pass filter. In some embodiments, the processing device 120 mayobtain the first image by subtracting the gray value f_(H)(i,j) of eachpixel of the second image from the gray value f(i,j) of a correspondingpixel in the image.

In 2206, the processing device 120 (e.g., the transform modeldetermination module 2106) may determine multiple ranges of gray valuesrepresented in the first image.

In some embodiments, the first image may include a target region and abackground region. The target region in the first image may include arepresentation of at least a portion of the subject. The backgroundregion may include a representation of air. The target region in thefirst image may be formed based on projection data generated afterX-rays pass through the subject and detected by a detector in an imagingdevice. The background region in the first image may be formed based onprojection data generated after X-rays pass through air while not passthrough the subject and detected by the detector in the imaging device.

In some embodiments, the processing device 120 may determine themultiple ranges of gray values based on a segmentation threshold betweenthe target region and the background region. For example, the multipleranges may include at least one first range from a minimum gray valueamong the gray values of pixels in the first image to the segmentationthreshold between the target region and the background region and atleast one second range from the segmentation threshold between thetarget region and the background region to a maximum gray value amongthe gray values of pixels in the first image. The at least one firstrange may include gray values of pixels in the target regionrepresenting the subject and the at least one second range may includegray values in the background region in the first image.

For example, FIG. 26 shows a grayscale histogram of a low-frequencyimage according to some embodiments of the present disclosure. As shownin FIG. 26, the abscissa of the grayscale histogram represents the grayvalues of the pixels in the first image. The ordinate represents a countof pixels with a certain gray value in the low-frequency image (e.g.,the first image). The at least one first range may be a range of [f_(L)Min,nPos] including gray values of pixels in the target regionrepresenting at least a portion of a subject and the at least one secondrange may be a range of (nPos,f_(L) Max]) including gray values in abackground region in the first image. f_(L) Min refers to the minimumgray value among the gray values of pixels in the first image. f_(L) Maxrefers to the maximum gray value among the gray values of pixels in thefirst image. nPos refers to the segmentation threshold between thetarget region and the background region.

In some embodiments, the at least one first range may include multiplethird ranges corresponding to different portions (e.g., a tissue ofdifferent types, such as the lung, the muscle, a low-density bone, ahigh-density bone) of the subject. The processing device 120 may dividethe at least one first range into the multiple third ranges based on aratio of a volume of each portion to a volume of the subject. Themultiple ranges may include the at least one second range and themultiple third ranges. For example, the processing device 120 maydetermine one or more dividing points (e.g., (x1,x2) as shown in FIG.26) between the multiple third ranges based on the ratio of the volumeof each portion to the volume of the subject.

In some embodiments, the one or more third ranges may include ahigh-attenuation range (e.g., [f_(L) Min,x1] as shown in FIG. 26), anintermediate range (e.g., (x1, x2] as shown in FIG. 26), and alow-attenuation range (e.g., (x2, nPos] as shown in FIG. 26). As usedherein, the high-attenuation range may include gray values of pixels inthe first image that represent a portion of the subject whoseattenuation coefficient of X-ray exceeds a threshold (e.g., bonetissue). The low-attenuation range may include gray values of pixels inthe first image that represent a portion of the subject whoseattenuation coefficient of X-ray is less than a threshold (e.g.,muscular tissue). The intermediate range may include gray values ofpixels in the first image that represent a transition portion (e.g., atissue between the bone tissue and the muscular tissue) of the subjectbetween the portion of the subject corresponding to the high-attenuationrange and the portion of the subject corresponding to thelow-attenuation range.

More descriptions for determining the multiple ranges may be found inFIG. 23 and the descriptions thereof.

In 2208, the processing device 120 (e.g., the transform modeldetermination module 2106) may determine a local transform model basedat least in part on the multiple ranges of the gray values of the pixelsin the first image.

The local transform model may be configured to adjust at least a portionof the gray values of the pixels in the first image. For example, thelocal transform model may be used to equalize the gray values of thepixels in the first image. The local transform model may correspond todifferent local equalization intensities each of which is applicable forgray values in one of the multiple ranges.

In some embodiments, the processing device 120 may determine multipletransform sub-models each of which corresponds to one of the multipleranges based at least in part on the corresponding range of gray values.The processing device 120 may determine the local transform local basedon the multiple transform sub-models. For example, the local transformmodel may include the multiple transform sub-models. As another example,the multiple transform sub-models may be integrated into the localtransform sub-models. In some embodiments, the multiple transformsub-models may include a first transform sub-model corresponding to theat least one first range and a second transform sub-model correspondingto the at least one second range. In some embodiments, the multipletransform sub-models may include a transform sub-model corresponding tothe high-attenuation range, a transform sub-model corresponding to thelow-attenuation range, a transform sub-model corresponding to theintermediate range, and a transform sub-model corresponding to thebackground region of the subject.

In some embodiments, the local transform model may be denoted in theform of a function or a curve. The curve corresponding to the localtransform model may also be referred to as a target transform curve. Insome embodiments, the target transform curve may include multiplesections each of which includes a transform curve corresponding to oneof the multiple ranges of the gray values of pixels in the first image.In some embodiments, the multiple sections of the target transform curvemay be discontinuous. In some embodiments, the multiple sections of thetarget transform curve may be continuous. In some embodiments, thetarget transform curve may be obtained by fitting the multiple transformcurves corresponding to the multiple ranges of the gray values of pixelsin the first image.

In some embodiments, the processing device 120 may determine the localtransform model based on multiple local equalization intensities each ofwhich corresponds to one of the multiple ranges. For example, theprocessing device 120 may generate each of the multiple transformsub-models each of which corresponds to one of the multiple ranges basedat least in part on the corresponding local equalization intensity. Theprocessing device 120 may generate the local transform model based onthe multiple transform sub-models.

As a further example, the processing device 120 may determine a slope ofa transform curve corresponding to a specific range of the multipleranges based on a local equalization intensity corresponding to thespecific range and gray values in the specific range. The slopes of themultiple transform curves corresponding to the multiple ranges may bedifferent as different local equalization intensities. The processingdevice 120 may determine the transform curve corresponding to thespecific range of the multiple ranges based at least in part on theslope of the transform curve.

In some embodiments, the target transform curve may include the multipletransform curves each of which corresponds to one of the multipleranges. In some embodiments, the processing device 120 may obtain thelocal transform model (i.e., the target transform curve) by fitting themultiple transform curves corresponding to the multiple ranges of thegray values of pixels in the first image. For example, FIG. 27 shows atarget transform curve corresponding to the multiple ranges according tosome embodiments of the present disclosure. As shown in FIG. 27, thetarget transform curve includes multiple transform curves L1-L4corresponding to ranges (f_(L) Min, X1), (X1, X2), (X2, nPos), and(nPos, f_(L) Max), respectively. FIG. 28 shows a target transform curvecorresponding to the multiple ranges according to some embodiments ofthe present disclosure. As shown in FIG. 28, the target transform curvedenoted by a dotted line was determined by fitting the multipletransform curves L1-L4. The target transform curve shown in FIG. 28 maybe smoother than the target transform curve shown in FIG. 27. Exemplaryfitting techniques may include the least square, a quadratic polynomialfitting, a cubic polynomial fitting, a semi-logarithmic fittingregression, a log-Log fitting regression, a logit-log fittingregression, a four-parameter fitting, a cubic spline interpolation, etc.

More descriptions for determining the local transform model may be foundin FIG. 24 and the descriptions thereof.

In 2210, the processing device 120 (e.g., the image generation module2104) may determine a third image by processing the first image usingthe local transform model.

In some embodiments, the processing device 120 may input the gray valuesof the pixels in the first image into the local transform model. Thelocal transform model may generate gray values of pixels in the thirdimage based on the inputted gray values. In some embodiments, the localtransform model may include the multiple transform sub-models (e.g., themultiple transform curves) each of which corresponds to the multipleranges of the gray values of pixels in the first image. The processingdevice 120 may adjust gray values of pixels in each of a portion of themultiple ranges using a corresponding transform sub-model to obtain thethird image. Gray values of pixels in each of the rest portion of themultiple ranges may be unchanged or not adjusted.

As a further example, the local transform model may be denoted as thetarget transform curve. The gray value f(i,j) of a pixel in the firstimage may be transformed using a transform curve (e.g., the targettransform curve or a section of the target transform curve) to obtainthe gray value g_(L)(i,j)=curve(f(i,j)) of a corresponding pixel in thethird image, where curve refers to the target transform curve, (i,j)refers the position (e.g., coordinates) of the pixel in the image or thethird image. In some embodiments, if only one section of the targettransform curve is used to transform the first image, only the pixelswhose gray values are in one of the multiple ranges corresponding to thesection of the target transform curve may be transformed, while thepixels whose gray values are in other ranges of the multiple ranges maybe constant.

In some embodiments, the different sections of the target transformcurve corresponding to different ranges may be applied to transform grayvalues of corresponding ranges. For example, a transform curvecorresponding to the high-attenuation range may be used to transformgray values of pixels in a high-attenuation region in the imageincluding pixels whose gray values are in the high-attenuation range. Atransform curve corresponding to the low-attenuation range and may beused to transform gray values of pixels in a low-attenuation region inthe first image, while gray values of pixels in other regions (e.g., thebackground region or a transition region between the high-attenuationregion and the low-attenuation region) in the first image may beunchanged.

In 2212, the processing device 120 (e.g., the image generation module2104) may determine a target image of the subject based on the thirdimage and the second image.

In some embodiments, the processing device 120 may determine the targetimage by fusing the third image (also referred to as a firstlow-frequency image) and the second image. For example, the processingdevice 120 may add the gray value of each pixel in the third image andthe gray value of the corresponding pixel in the second image to obtainthe target image. As used herein, corresponding pixels in multipleimages (e.g., the third image and the second image) may refer to pixelsthat are located in the same position in the multiple images (e.g., thethird image and the second image).

In some embodiments, the processing device 120 may transform the secondimage (i.e., the high-frequency image) to obtain a fourth image (alsoreferred to as a first high-frequency image). The processing device 120may determine the target image by fusing the third image and the fourthimage. In some embodiments, the processing device 120 may determine thefourth image based on a transform ratio between corresponding pixels inthe third image and the first image. Each pixel in the third image maycorrespond to a transform ratio. The transform ratio betweencorresponding pixels in the third image and the first image may bedetermined according to Equation (1):

Ratio=|g _(L)(i,j)/f _(L)(i,j)−1|+1  (1),

where g_(L)(i,j) denotes the gray value of a pixel in the third image,f_(L)(i,j) denotes the gray value of a corresponding pixel in the firstimage.

In some embodiments, the fourth image may be obtained according toEquation (2):

g _(H)(i,j)=f _(H)(i,j)/Ratio²  (2)

where g_(H)(i,j) denotes the gray value of a pixel in the fourth image,and f_(H)(i,j) denotes the gray value of a corresponding pixel in thesecond image.

In some embodiments, the processing device 120 may transform the thirdimage using a global transform model to obtain a fifth image (alsoreferred to as a transformed third image or second low-frequency image).The processing device 120 may determine the target image based on thefifth image and the second image. For example, the processing device 120may determine the target image by fusing the fifth image and the secondimage.

As another example, the processing device 120 may transform the secondimage (i.e., the high-frequency image) based on a transform ratiobetween corresponding pixels in the fifth image and the first image toobtain a sixth image (also referred to as second high-frequency image).The processing device 120 may determine the target image by fusing thefifth image and the sixth image. The processing device 120 may determinethe transform ratio between corresponding pixels in the fifth image andthe first image according to Equation (1). The processing device 120 maydetermine the sixth image based on the transform ratio between eachcorresponding pixels in the fifth image and the first image according toEquation (2). More descriptions for determining the target image may befound in FIG. 25 and the descriptions thereof.

Accordingly, the systems and methods of the present disclosure maydetermine a local transform model based on different local equalizationintensities that corresponding to different ranges of gray values in animage, which may be applicable to equalize gray values in differentranges in an image (especially for tissues including large differencesin density), thus more image information in the image may be displayed,which may improve the display of the image. The window width and thewindow level for displaying the equalization image may not need to beadjusted repeatedly, which may improve the efficiency of an operator forobserving the equalization image.

It should be noted that the above description is merely provided forillustration, and not intended to limit the scope of the presentdisclosure. For persons having ordinary skills in the art, multiplevariations and modifications may be made under the teachings of thepresent disclosure. However, those variations and modifications do notdepart from the scope of the present disclosure. In some embodiments,one or more operations may be omitted and/or one or more additionaloperations may be added.

FIG. 23 is a flowchart illustrating an exemplary process for determiningmultiple ranges of gray values in an image according to some embodimentsof the present disclosure. In some embodiments, process 2300 may beimplemented as a set of instructions (e.g., an application) stored inthe storage device 130, storage 220, or storage 390. The processingdevice 120, the processor 210 and/or the CPU 340 may execute the set ofinstructions, and when executing the instructions, the processing device120, the terminal 140, the processor 210 and/or the CPU 340 may beconfigured to perform the process 2300. The operations of theillustrated process presented below are intended to be illustrative. Insome embodiments, the process 2300 may be accomplished with one or moreadditional operations not described and/or without one or more of theoperations discussed. Additionally, the order of the operations of theprocess 2300 illustrated in FIG. 23 and described below is not intendedto be limiting. Operation 2206 in FIG. 22 may be performed according toprocess 2300 as illustrated in FIG. 23.

In 2302, the processing device 120 may determine a segmentationthreshold between a target region and a background region in an image ofa subject. The image may be a low-frequency image of the subject asdescribed elsewhere in the present disclosure. The low-frequency imageof the subject may be obtained as described in connection with operation2204 in FIG. 2000.

In some embodiments, the processing device 120 may determine thesegmentation threshold between the target region and the backgroundregion based on a gray value distribution of the first image accordingto process 1000.

In some embodiments, the processing device 120 may determine thesegmentation threshold between the target region and the backgroundregion according to process 500.

In some embodiments, the processing device 120 may determine thesegmentation threshold using an image segmentation technique. Exemplaryimage segmentation techniques may include using a maximum inter-classvariance algorithm, an adaptive threshold segmentation algorithm, amaximum entropy threshold segmentation algorithm, an iterative thresholdsegmentation algorithm, etc. For example, using the maximum inter-classvariance algorithm, the processing device 120 may determine a grayscalehistogram of the image. The grayscale histogram of the image may includetwo peaks and a trough between the two peaks. The processing device 120may designate the gray value corresponding to the trough as thesegmentation threshold.

In some embodiments, the processing device 120 may determine thesegmentation threshold based on at least one of a maximum gray value(denoted as f_(L) Max), a minimum gray value (denoted as f_(L) Min), awidth of the grayscale histogram (i.e., a difference between the maximumgray value and the minimum gray value (f_(L) Max−f_(L) Min). Forexample, the processing device 120 may determine the segmentationthreshold (e.g., nPos as shown in FIG. 26) by multiplying the maximumgray value of the image by a proportion or percentage (such as 90%). Asanother example, the processing device 120 may determine a gray value bymultiplying the width of the grayscale histogram (i.e., (f_(L) Max−f_(L)Min)) of the image with a proportion or percentage (such as 90%). Theprocessing device 120 may determine the segmentation threshold by addingthe gray value to the minimum gray value (f_(L) Min) of the first image.In some embodiments, the proportion or percentage may be set by a useror according to a default setting of the medical system 100. Forexample, the proportion or percentage may be determined according to alarge number of experimental calculations or historical experience.

In some embodiments, the processing device 120 may determine thesegmentation threshold using a threshold identification model. Thethreshold identification model may be obtained by a processing devicethat is the same as or different from the processing device 120 using aplurality of training samples based on a training algorithm (e.g., abackpropagation algorithm, a gradient descent algorithm, etc.). Each ofthe plurality of training samples may include a segmentation thresholdof an image and the image. The threshold identification model may beobtained by the processing device training a machine learning modelusing the plurality of training samples. The machine learning model maybe described elsewhere in the present disclosure. The processing device120 may input the gray value distribution (e.g., a grayscale histogram)of the image or the image into the threshold identification model, thenthe segmentation threshold between the target region and the backgroundregion may be determined. In 2304, the processing device 120 maydetermine, based on the segmentation threshold, at least one first rangeand at least one second range corresponding to the target region and thebackground region, respectively.

The at least one first range may be from the minimum gray value amongthe gray values of pixels in the image to the segmentation thresholdbetween the target region and the background region. The at least onesecond range may be from the segmentation threshold between the targetregion and the background region to the maximum gray value among thegray values of pixels in the image. The at least one first range mayinclude gray values of pixels in the target region representing thesubject and the at least one second range may include gray values in thebackground region in the image.

In 2306, the processing device 120 may determine multiple third rangesbased on the at least one first range.

According to attenuation coefficients of X-ray, the type of tissue inthe subject may include a high-attenuation portion, a low-attenuationportion, a transition portion between the high-attenuation portion andthe low-attenuation portion, or the like, or a combination thereof. Forexample, the high-attenuation portion may have a greater attenuationcoefficient of X-rays, such as bone tissue. The low-attenuation portionmay have a smaller attenuation coefficient of X-rays such as muscletissue. The transition portion may be a portion between bone tissue andmuscle tissue. The multiple third ranges may include a high-attenuationrange corresponding to the high-attenuation portion, a low-attenuationrange corresponding to the low-attenuation portion, an intermediatecorresponding to the transition portion, or the like, or a combinationthereof.

The image may include a high-attenuation region, a low-attenuationregion, a transition region between the high-attenuation region and thelow-attenuation region, or the like, or a combination thereof, includinga representation of the high-attenuation portion, the low-attenuationportion, the transition portion between the high-attenuation portion andthe low-attenuation portion, or the like, or a combination thereof,respectively.

The high-attenuation region may include pixels whose gray values are ina high-attenuation range. The low-attenuation region may include pixelswhose gray values are in a low-attenuation range. The transition regionbetween the high-attenuation region and the low-attenuation region mayinclude pixels whose gray values are in an intermediate range. Themultiple third ranges may include the high-attenuation range, thelow-attenuation range, and the intermediate range.

In some embodiments, the processing device 120 may divide the at leastone first range into the multiple third ranges based on ratios ofvolumes of tissue of different types to the total volume of the subject.For example, the processing device 120 may determine a ratio (denoted asArea_(H)) of the volume of the high-attenuation portion to the totalvolume of the subject, a ratio (denoted as Area_(L)) of the volume ofthe low-attenuation portion to the total volume of the subject, and/or aratio of the volume of the transition portion to the total volume of thesubject. The processing device 120 may determine a first dividing point(also referred to as first inflection point) between thehigh-attenuation range and the intermediate range based on the ratioArea_(H) and/or a second dividing point between the intermediate rangeand the low-attenuation range based on the ratio Area_(L) according toEquation (3) and Equation (4), respectively:

x1=f _(L) Min+Area_(H)×HistogramSpan  (3)

x2=nPos−Area_(L)×HistogramSpan  (4),

where x1 refers to the first dividing point, x2 refers to the seconddividing point, f_(L) Min denotes the minimum gray value of the firstimage, HistogramSpan denotes the width of a grayscale histogram of thetarget region, nPos denotes the segmentation threshold of the targetregion and the background region, Area_(H) denotes the ratio of thehigh-attenuation portion in the subject, and Area_(L) denotes the ratioof the low-attenuation portion in the subject. The processing device 120may determine the one or more third ranges may include thehigh-attenuation range (e.g., (f_(L) Min, x1] as shown in FIG. 26,), theintermediate range ((x1, x2] as shown in FIG. 26), and thelow-attenuation range ((x2, nPos] as shown in FIG. 26) based on thefirst dividing point and the second dividing point. The high-attenuationrange may be from the minimum gray value to the first dividing point.The low-attenuation range may be from the second dividing point to thesegmentation threshold. The intermediate range may be from the firstdividing point to the second dividing point.

In some embodiments, the ratios Area_(H) and/or Area_(L) of thehigh-attenuation portion and low-attenuation portion may be determinedbased on a ratio recognition model. The ratio recognition model may beobtained by a processing device that is the same as or different fromthe processing device 120 using a plurality of training samples based ona training algorithm (e.g., a backpropagation algorithm, a gradientdescent algorithm, etc.). Each of the plurality of training samples mayinclude ratios of the high-attenuation portion and/or low-attenuationportion represented in an image and the image. The ratio recognitionmodel may be obtained by the processing device training a machinelearning model using the plurality of training samples. The machinelearning model may be described elsewhere in the present disclosure.

The sum of ratios Area_(H) and Area_(L) does not exceed 1.

In some embodiments, the width HistogramSpan of the grayscale histogramof the target region may be determined according to Equation (5):

HistogramSpan=nPos−f _(L) Min  (5).

In some embodiments, the division of the range of the gray value may beautomatically performed by the transform model determination module 2106or may be performed by an operator via a user interface.

It should be noted that the above description is merely provided forillustration, and not intended to limit the scope of the presentdisclosure. For persons having ordinary skills in the art, multiplevariations and modifications may be made under the teachings of thepresent disclosure. However, those variations and modifications do notdepart from the scope of the present disclosure. In some embodiments,one or more operations may be omitted and/or one or more additionaloperations may be added.

FIG. 24 is a flowchart illustrating an exemplary process for determininga local transform model for a low-frequency image according to someembodiments of the present disclosure. In some embodiments, process 2400may be implemented as a set of instructions (e.g., an application)stored in the storage device 130, storage 220, or storage 390. Theprocessing device 120, the processor 210 and/or the CPU 340 may executethe set of instructions, and when executing the instructions, theprocessing device 120, the terminal 140, the processor 210 and/or theCPU 340 may be configured to perform the process 2400. The operations ofthe illustrated process presented below are intended to be illustrative.In some embodiments, the process 2400 may be accomplished with one ormore additional operations not described and/or without one or more ofthe operations discussed. Additionally, the order of the operations ofthe process 2400 illustrated in FIG. 24 and described below is notintended to be limiting. Operation 2208 in FIG. 22 may be performedaccording to process 2400 as illustrated in FIG. 24.

In 2402, the processing device 120 may obtain local equalizationintensities corresponding to multiple ranges of gray values of pixels inan image. The image may be a low-frequency image of the subject asdescribed elsewhere in the present disclosure. The low-frequency imageof the subject may be obtained as described in connection with operation2204 in FIG. 22. The multiple ranges of gray values of pixels in theimage may be determined as described elsewhere in the present disclosure(e.g., FIGS. 22 and 23 and the descriptions thereof).

Each of the local equalization intensities may correspond to one of themultiple ranges. For example, the multiple ranges may include ahigh-attenuation range corresponding to the high-attenuation portion ofthe subject, a low-attenuation range corresponding to thelow-attenuation portion of the subject, and an intermediatecorresponding to the transition portion of the subject. The localequalization intensities may include an equalization intensityIntensity_(H) corresponding to the high-attenuation range, anequalization intensity Intensity_(M) corresponding to the intermediaterange, an equalization intensity Intensity_(M) corresponding to thelow-attenuation range.

In some embodiments, the local equalization intensities may be set by auser or according to a default setting of the medical system 100. Theprocessing device 120 may obtain the local equalization intensities froma storage device (e.g., the storage device 130). For example, the localequalization intensities may be inputted by an operator (such as adoctor) based on clinical experience or a large amount of experimentaldata via a user interface implemented on a terminal device (e.g., theterminal 140) or an imaging device (e.g., the medical device 110). Asanother example, the range of a local equalization intensity may be setto any value from 0 to 20. In some embodiments, the operator may adjustat least one of the local equalization intensities corresponding to oneof the multiple ranges based on the effect of the target image. The userinterface of the medical device 110 may include option controlscorresponding to the equalization intensity of the high-attenuationrange, the equalization intensity of the intermediate range, theequalization intensity of the low-attenuation range, and theequalization intensity of the image. For example, the option controlsmay include an input text box, sliding button, etc. The operator mayenter or select different equalization intensities through the optioncontrols.

In 2404, the processing device 120 may determine, based on the localequalization intensities corresponding to the multiple ranges, thetarget transform curve.

In some embodiments, the processing device 120 may determine, based onthe local equalization intensities, multiple transform curves each ofwhich corresponds to one of the multiple ranges. The processing device120 may determine the target transform curve based on the multipletransform curves. For example, the target transform curve may includethe multiple transform curves each of which corresponds to one of themultiple ranges. As another example, the processing device 120 may fitthe multiple transform curves to generate the target transform curve.

In some embodiments, the slope of a transform curve and a localequalization intensity corresponding to one of the multiple ranges ofthe gray values of pixels in the image may be positively or negativelycorrelated. The processing device 120 may determine the slope of thetransform curve based on the local equalization intensity correspondingto one of the multiple ranges of the gray values of pixels in the image.Further, the processing device 120 may determine the transform curvebased on an end point of a transform curve corresponding to a previousrange of the multiple ranges. For example, as shown in FIG. 26, theprevious range of the intermediate range from inflection point X1 toinflection point X2 may be the high-attenuation range. The previousrange of the low-attenuation range may be the intermediate range. Insome embodiments, the processing device 120 may determine the transformcurve corresponding to the high-attenuation range based on the slopecorresponding to the transform curve and the mean gray value f_(L)Meanof the image.

In some embodiments, the relationship between the slope of a transformcurve corresponding to one of the multiple ranges of gray values in theimage and the equalization intensity corresponding to the range of grayvalues may be determined by a user or according to a default setting ofthe medical system 100. In some embodiments, the relationship betweenthe slope of a transform curve corresponding to one of the multipleranges of gray values in the image and the equalization intensitycorresponding to the range of gray values may be determined based on alarge amount of data, or based on clinical experience.

In some embodiments, the transform curve corresponding to each range ofgray values may be determined according to the following Equations (6)to (9):

Curve 1: y _(H) k1*x+b  (6)

Curve 2: y _(M) =k2*(x−x1)+y  (7)

Curve 3: y _(L) =k3*(x−x2)+y2  (8)

Curve 4: y _(B) =k4*(x−x3)+y3  (9),

where curve 1 corresponds to the high-attenuation range, curve 2corresponds to the intermediate range, curve 3 corresponds to thelow-attenuation range, and curve 4 corresponds to the second rangecorresponding to the background region.

The high-attenuation range may be [f_(L) Min,x1], where b denotes a meanof gray values in the image, denoted as f_(L)Mean, k1 refers to theslope of curve 1, that is equal to 1-0.04*Intensity_(H). Intensity_(H)refers to the local equalization intensity corresponding to thehigh-attenuation range.

The intermediate range may be [x1+1,x2], y1 denotes Curve 1(x1)=k1*x1+b,i.e., the value of y_(H) when x is equal to x1, k2 refers to the slopeof Curve 2, that is equal to 1−0.04*Intensity_(M), and x1 refers to thefirst dividing point between the high-attenuation range and theintermediate range. Intensity_(M) refers to the local equalizationintensity corresponding to the intermediate range.

The low-attenuation range may be [x2+1, nPos], y₂ denotes Curve2(x2)=k2*(x2−x1)+y1, i.e., the value of y_(M) when x is equal to x2, k3refers to the slope of Curve 3, that is equal to 1−0.04*Intensity_(L).nPos refers to the segmentation threshold between the target region andthe background region. Intensity_(L) refers to the local equalizationintensity corresponding to the low-attenuation range. x2 refers to thesecond dividing point between the low-attenuation range and theintermediate range.

The second range may be[nPos+1,f_(L) Max], x3 denotes f_(L) Max, y3denotes Curve 3(nPos)=k3*(nPos−x2)+y2, i.e., the value of y_(L) when xis equal to nPos, k4 refers to the slope of Curve 3, that is equal to(f_(L) Max−Curve 3(nPos))/(f_(L) Max−nPos).

One or more local equalization intensities may be changed, and the slopeof the transform curves determined based on the one or more localequalization intensities may be changed accordingly, thereby causing thechange of one or more transform curves determined based on the changedlocal equalization intensities. When the gray values of the image aretransformed by the transform curves, the display effect of the image maybe changed.

In some embodiments, the user (e.g., an operator) may adjust the localequalization intensities corresponding to different ranges according tothe display of the target image. For example, FIG. 29 illustrates targetimages obtained based on transform curves corresponding to thehigh-attenuation range according to some embodiments of the presentdisclosure. Image 1 as shown in FIG. 29 is a target image obtained usinga transform curve determined based on a local equalization intensitycorresponding to the high-attenuation range that is equal to 0. Image 2as shown in FIG. 29 is a target image obtained using a transform curvedetermined based on a local equalization intensity corresponding to thehigh-attenuation range that is equal to 20. Image 1 and image 2 includerepresentations of the same subject (a portion of a leg). The grayvalues corresponding to the high-attenuation range, i.e., the bonetissue in image 2 change with respect to the gray values correspondingto the high-attenuation range, i.e., the bone tissue in image 1. Asanother example, FIG. 30 illustrates target images obtained based ontransform curves corresponding to the low-attenuation range according tosome embodiments of the present disclosure. Image 1 as shown in FIG. 30is a target image obtained using a transform curve determined based on alocal equalization intensity corresponding to the low-attenuation rangethat is equal to 0. Image 2 as shown in FIG. 30 is a target imageobtained using a transform curve determined based on a localequalization intensity corresponding to the low-attenuation range thatis equal to 20. The gray values corresponding to the low-attenuationrange, i.e., the muscle tissue in image 2 change with respect to thegray values corresponding to the low-attenuation range, i.e., the muscletissue in image 1. The operator may adjust the local equalizationintensities of different ranges according to actual needs.

It should be noted that the above description is merely provided forillustration, and not intended to limit the scope of the presentdisclosure. For persons having ordinary skills in the art, multiplevariations and modifications may be made under the teachings of thepresent disclosure. However, those variations and modifications do notdepart from the scope of the present disclosure. In some embodiments,one or more operations may be omitted and/or one or more additionaloperations may be added.

FIG. 25 is a flowchart illustrating an exemplary process for processingan image according to some embodiments of the present disclosure. Insome embodiments, process 2500 may be implemented as a set ofinstructions (e.g., an application) stored in the storage device 130,storage 220, or storage 390. The processing device 120, the processor210, and/or the CPU 340 may execute the set of instructions, and whenexecuting the instructions, the processing device 120, the terminal 140,the processor 210, and/or the CPU 340 may be configured to perform theprocess 2500. The operations of the illustrated process presented beloware intended to be illustrative. In some embodiments, the process 2500may be accomplished with one or more additional operations not describedand/or without one or more of the operations discussed. Additionally,the order of the operations of the process 2500 illustrated in FIG. 25and described below is not intended to be limiting. Operation 2212 inFIG. 22 may be performed according to process 2500 as illustrated inFIG. 25.

In 2502, the processing device 120 (e.g., the image generation module2104) may process a third image using a global transform model to obtaina transformed third image.

The third image may be obtained as described in connection withoperation 2210 in FIG. 22. For example, the processing device 120 maydetermine the third image by transforming a first image (i.e., alow-frequency image) using a local transform model. The third image mayalso be referred to as a first low-frequency image.

In some embodiments, the global transform model may be determined basedon a mean gray value of the first image denoted as g_(L)Mean and aglobal equalization intensity denoted as Intensity_(whole) correspondingto all gray values in the first image.

In some embodiments, the global transform model may be in the form of aglobal transform curve. The global transform curve may be determinedbased on the global equalization intensity Intensity_(whole) of thefirst image and/or the mean gray value g_(L)Mean of the first image. Forexample, the slope of the global transform curve may be determined basedon the global equalization intensity Intensity_(whole). Further, theglobal transform curve may be denoted as Equation (10) as follows:

y _(w) =k*x+b  (10),

where k=1−0.02×Intensity_(whole), b refers to the mean gray value of thefirst image g_(L)Mean, Intensity_(whole) denotes the global equalizationintensity.

The transformed third image may be obtained according to Equation (10).Gray values in the transformed third image may beg′_(L)(i,j)=k*g_(L)(i,j)+b, where g_(L)(i,j) denotes the gray value ofthe third image. If the global equalization intensity is 0, the thirdimage may not be transformed.

In 2504, the processing device 120 (e.g., the transform modeldetermination module 2106) may determine transform ratios each of whichcorresponds to two corresponding pixels in the transformed third imageand the first image, based on a gray value of each of pixels in thefifth image and a gray value of each of pixels in the first image.

The transform ratio between corresponding pixels in the transformedthird image and the first image may be determined according to Equation(11):

Ratio=|g _(L)(i,j)/f _(L)(i,j)−1|+1  (11),

where g_(L)(i,j) denotes the gray value of a pixel in the transformedthird image, and f_(L)(i,j) denotes the gray value of a correspondingpixel in the first image.

In 2506, the processing device 120 (e.g., the image generation module2104) may determine a fourth image by processing the second image basedon the transform ratios.

In some embodiments, the fourth image may be obtained according toEquation (12):

g _(H)(i,j)=f _(H)(i,j)/Ratio²  (12),

where g_(H)(i,j) denotes the gray value of a pixel in the fourth image,and f_(H)(i,j) denotes the gray value of a corresponding pixel in thesecond image.

In 2508, the processing device 120 (e.g., the image generation module2104) may determine the target image by fusing the fourth image and thetransformed third image (or the third image).

In some embodiments, the processing device 120 may add the gray value ofeach pixel in the transformed third image (or the third image) and thegray value of the corresponding pixel in the fourth image to obtain thetarget image.

It should be noted that the above description is merely provided forillustration, and not intended to limit the scope of the presentdisclosure. For persons having ordinary skills in the art, multiplevariations and modifications may be made under the teachings of thepresent disclosure. However, those variations and modifications do notdepart from the scope of the present disclosure. In some embodiments,one or more operations may be omitted and/or one or more additionaloperations may be added.

FIG. 31 is a schematic diagram illustrating an exemplary processingdevice for processing image according to some embodiments of the presentdisclosure. As shown, the processing device 120 may include a secondacquisition module 3102, an equalization module 3104, a display module3106, and a storage module 3108. In some embodiments, the secondacquisition module 3102, the equalization module 3104, the displaymodule 3106, and the storage module 3108 may be connected to and/orcommunicate with each other via a wireless connection (e.g., a network),a wired connection, or a combination thereof.

The second acquisition module 3102 may obtain an image of a subject andone or more equalization intensities. In some embodiments, the image maybe a medical image. More descriptions for the image may be foundelsewhere in the present disclosure (e.g., FIG. 22 and the descriptionsthereof). In some embodiments, the image may be displayed on the usergraphical interface implemented on a terminal (e.g., the terminal 140).

In some embodiments, the equalization intensity may be a parameter forprocessing images. More descriptions for the equalization intensity maybe found elsewhere in the present disclosure (e.g., FIGS. 22-25, and thedescriptions thereof). Specifically, one or more equalizationintensities may be obtained through input controls on the user graphicalinterface. For example, the user may click on the “equalizationintensity” button on the user graphical interface, so that the secondacquisition module 3102 may obtain the equalization intensity anddisplay the equalization intensity on the user graphical interface. Insome embodiments, the value of the equalization intensity may beadjusted. In some embodiments, the range of the equalization intensityon the user graphical interface may be 0-20. The user may enterdifferent values on the user graphical interface to adjust the value ofthe equalization intensity.

The equalization module 3104 may generate an equalization image based onone or more equalization intensities and one or more gray values of theimage. In some embodiments, the equalization image may be obtained bythe processing device 120 performs an equalization operation on theimage.

In some embodiments, the equalization module 3104 may process the imageusing a low-pass filter to obtain a low-frequency image of the image.The equalization module 3104 may obtain a high-frequency image bysubtracting the gray value of each pixel in the low-frequency image fromthe gray value of the corresponding pixel in the image.

In some embodiments, the equalization module 3104 may equalize ortransform the low-frequency image to obtain a first low-frequency imageusing a local transform model that is determined based on one or morelocal equalization intensities as described elsewhere in the presentdisclosure. The equalization module 3104 may determine the equalizationimage by fusing the high-frequency image and the first low-frequencyimage. The local transform model may be in the form of a targettransform curve including multiple sections each of which corresponds toone of the multiple ranges of gray values of pixels in the image. Eachof the multiple sections of the target transform curve may be determinedbased on one of the one or more local equalization intensitiescorresponding to the one of the multiple ranges of gray values of pixelsin the image as described elsewhere in the present disclosure (e.g.,FIG. 22, and the descriptions thereof). For example, the slope of eachsection of the multiple sections may be determined based on one of theone or more local equalization intensities corresponding to the one ofthe multiple ranges of gray values of pixels in the image.

In some embodiments, the equalization module 3104 may equalize ortransform the high-frequency image to obtain a first high-frequencyimage as described elsewhere in the present disclosure that isdetermined based on local one or more equalization intensities (e.g.,FIGS. 22 and 25, and the descriptions thereof). The equalization module3104 may determine the equalization image by fusing the firsthigh-frequency image and the first low-frequency image or thelow-frequency.

In some embodiments, the equalization module 3104 may equalize ortransform the first low-frequency image to obtain a second low-frequencyimage using a global transform model as described elsewhere in thepresent disclosure that is determined based on local one or moreequalization intensities (e.g., FIG. 25, and the descriptions thereof).The equalization module 3104 may determine the equalization image byfusing the high-frequency image and the second low-frequency image.

The display module 3106 may dynamically display the transform curveand/or the equalization image corresponding to the equalizationintensity. In some embodiments, the transform model (e.g., the localtransform curve) and the equalization image that is determined based onthe transform model may be dynamically displayed on the user graphicalinterface. For example, if an operator (such as a doctor) adjusts thevalue of the equalization intensities, the change of the local transformcurve and the equalization image may be dynamically displayed on theuser graphical interface. In some embodiments, the display module 3106may dynamically display the local transform curve and/or theequalization image corresponding to the equalization intensities.

The storage module 3108 may be configured to store data, instructions,and/or any other information for image equalization. For example, thestorage module 3108 may store the image of the subject, transformmodels, etc. In some embodiments, the storage module 2108 may be thesame as the storage device 130 in configuration.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. Apparently, for persons having ordinary skills inthe art, multiple variations and modifications may be conducted underthe teachings of the present disclosure. However, those variations andmodifications do not depart from the scope of the present disclosure.For example, some other components/modules may be added into and/oromitted from the processing device 120.

FIG. 32 is a flowchart illustrating an exemplary process for processingan image according to some embodiments of the present disclosure. Insome embodiments, process 3200 may be implemented as a set ofinstructions (e.g., an application) stored in the storage device 130,storage 220, or storage 390. The processing device 120, the processor210, and/or the CPU 340 may execute the set of instructions, and whenexecuting the instructions, the processing device 120, the terminal 140,the processor 210, and/or the CPU 340 may be configured to perform theprocess 3200. The operations of the illustrated process presented beloware intended to be illustrative. In some embodiments, the process 3200may be accomplished with one or more additional operations not describedand/or without one or more of the operations discussed. Additionally,the order of the operations of the process 3200 illustrated in FIG. 32and described below is not intended to be limiting.

In 3202, the processing device 120 (e.g., the second acquisition module3102) may obtain an image of a subject and one or more equalizationintensities.

In some embodiments, the image may be a medical image. More descriptionsfor the image may be found elsewhere in the present disclosure (e.g.,FIG. 22 and the descriptions thereof). In some embodiments, the imagemay be displayed on the user graphical interface implemented on aterminal (e.g., the terminal 140).

In some embodiments, the equalization intensity may be a parameter forprocessing images. More descriptions for the equalization intensity maybe found elsewhere in the present disclosure (e.g., FIGS. 22-25, and thedescriptions thereof). Specifically, one or more equalizationintensities may be obtained through input controls on the user graphicalinterface. For example, the user may click on the “equalizationintensity” button on the user graphical interface, so that the secondacquisition module 3102 may obtain the equalization intensity anddisplay the equalization intensity on the user graphical interface. Insome embodiments, the value of the equalization intensity may beadjusted. In some embodiments, the range of the equalization intensityon the user graphical interface may be 0-20. The user may enterdifferent values on the user graphical interface to adjust the value ofthe equalization intensity.

In 3204, the processing device 120 (e.g., the equalization module 3104)may generate an equalization image based on a transform model associatedwith one or more equalization intensities and one or more gray values ofthe image.

In some embodiments, the equalization image may be obtained by theprocessing device 120 performs an equalization operation on the image.

In some embodiments, the processing device 120 may process the imageusing a low-pass filter to obtain a low-frequency image of the image.The processing device 120 may obtain a high-frequency image bysubtracting the gray value of each pixel in the low-frequency image fromthe gray value of the corresponding pixel in the image.

In some embodiments, the processing device 120 may equalize or transformthe low-frequency image to obtain a first low-frequency image using alocal transform model that is determined based on one or more localequalization intensities as described elsewhere in the presentdisclosure. The processing device 120 may determine the equalizationimage by fusing the high-frequency image and the first low-frequencyimage. The local transform model may be in the form of a targettransform curve including multiple sections each of which corresponds toone of the multiple ranges of gray values of pixels in the image. Eachof the multiple sections of the target transform curve may be determinedbased on one of the one or more local equalization intensitiescorresponding to the one of the multiple ranges of gray values of pixelsin the image as described elsewhere in the present disclosure (e.g.,FIG. 22, and the descriptions thereof). For example, the slope of eachsection of the multiple sections may be determined based on one of theone or more local equalization intensities corresponding to the one ofthe multiple ranges of gray values of pixels in the image.

In some embodiments, the processing device 120 may equalize or transformthe high-frequency image to obtain a first high-frequency image asdescribed elsewhere in the present disclosure that is determined basedon local one or more equalization intensities (e.g., FIGS. 22 and 25,and the descriptions thereof). The processing device 120 may determinethe equalization image by fusing the first high-frequency image and thefirst low-frequency image or the low-frequency.

In some embodiments, the processing device 120 may equalize or transformthe first low-frequency image to obtain a second low-frequency imageusing a global transform model as described elsewhere in the presentdisclosure that is determined based on local one or more equalizationintensities (e.g., FIG. 25, and the descriptions thereof). Theprocessing device 120 may determine the equalization image by fusing thehigh-frequency image and the second low-frequency image.

In 3206, the processing device 120 (e.g., the display module 3106) maydynamically display the transform model and/or the equalization imagecorresponding to the one or more equalization intensities.

In some embodiments, the transform model (e.g., the local transformcurve) and the equalization image that is determined based on thetransform model may be dynamically displayed on the user graphicalinterface. For example, if an operator (such as a doctor) adjusts thevalue of the equalization intensities, the change of the local transformcurve and the equalization image may be dynamically displayed on theuser graphical interface. In some embodiments, the display module 3106may dynamically display the local transform curve and/or theequalization image corresponding to the equalization intensities. Bydynamically displaying the local transform curve and the equalizationimage, the operator (e.g., a doctor) may observe in real-time whetherthe equalization image meets requirements, which may be convenient forthe operator to diagnose related diseases through the equalizationimage.

It should be noted that the above description is merely provided forillustration, and not intended to limit the scope of the presentdisclosure. For persons having ordinary skills in the art, multiplevariations and modifications may be made under the teachings of thepresent disclosure. However, those variations and modifications do notdepart from the scope of the present disclosure. In some embodiments,one or more operations may be omitted and/or one or more additionaloperations may be added.

FIG. 33 is a schematic diagram illustrating an exemplary processingdevice for processing an image according to some embodiments of thepresent disclosure. As shown, the processing device 120 may include anacquisition module 3302, a distribution of gray values module 3304, aparameter determination module 3306, a display module 3308, and astorage module 3310. In some embodiments, the acquisition module 3302,the distribution of gray values module 3304, the parameter determinationmodule 3306, the display module 3308, and the storage module 3310 may beconnected to and/or communicate with each other via a wirelessconnection (e.g., a network), a wired connection, or a combinationthereof.

The acquisition module 3302 may be configured to obtain an image of asubject. The image including a target region that includes arepresentation of at least a portion the subject.

In some embodiments, the image may include a medical image. For example,the image may include a DR image, an MR image, a PET image, a CT image,etc. The subject may be biological or non-biological. For example, thesubject may include a patient, a man-made object, etc. As anotherexample, the subject may include a specific portion, organ, and/ortissue of a patient. As still another example, the subject may include abreast. More descriptions for the subject and the image may be foundelsewhere in the present disclosure (e.g., FIGS. 1 and 5, and thedescriptions thereof).

The target region may be an image region identified from the image. Thetarget region (e.g., a region of interest (ROI)) may be the focus ofimage analysis for further processing. For example, the target regionmay represent a tumor, one or more nodules, polyps, a lesion, etc.

The distribution of gray values module 3304 may be configured todetermine a distribution of gray values of pixels in the target region.The distribution may indicate a count of pixels corresponding to eachgray value in the target region. In some embodiments, the distributionof the gray values of the pixels in the target region may include agrayscale histogram.

The parameter determination module 3306 may be configured to determine acharacteristic display parameter for the target region based on thedistribution of the gray values of the pixels in the target region. Thecharacteristic display parameter may include at least one of a windowwidth or a window level for the target region.

In some embodiments, the parameter determination module 3306 may performan iterative process to determine at least one of a window width or awindow level for the target region. The iterative process may include aplurality of iterations. For each iteration of the iterative process,the parameter determination module 3306 may determine an accumulatedcount of pixels in a current iteration by summing a count of pixelscorresponding to a current gray value with an accumulated count ofpixels determined in a prior iteration. The parameter determinationmodule 3306 may determine whether the accumulated count of pixels in thecurrent iteration satisfies a current condition. The parameterdetermination module 3306 may designate a gray value in the targetregion as the current gray value in response to determining that theaccumulated count of pixels in the current iteration does not satisfythe condition. The parameter determination module 3306 may terminate theiterative process until the accumulated count of pixels in the currentiteration satisfies the current condition and determine, based at leastin part on the current gray value, the at least one of the window widthor the window level for the target region. In the iterative process, theparameter determination module 3306 may update the condition with theincrease of the accumulated count of pixels.

In some embodiments, the parameter determination module 3306 may dividethe grayscale histogram into multiple intervals and perform theiteration process based on the multiple intervals.

The display module 3308 may be configured to display the image based onthe characteristic display parameter.

The storage module 3110 may be configured to store data, instructions,and/or any other information for image display. For example, the storagemodule 3110 may store the image of the subject, the characteristicdisplay parameters, etc. In some embodiments, the storage module 3110may be the same as the storage device 130 in configuration.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. Apparently, for persons having ordinary skills inthe art, multiple variations and modifications may be conducted underthe teachings of the present disclosure. However, those variations andmodifications do not depart from the scope of the present disclosure.For example, some other components/modules may be added into and/oromitted from the processing device The storage module 3310 may beconfigured to store data, instructions, and/or any other information forprocessing and displaying an image.

FIG. 34 is a flowchart illustrating an exemplary process for processingan image according to some embodiments of the present disclosure. Insome embodiments, process 3400 may be implemented as a set ofinstructions stored in the storage device 130, storage 220, or storage390. The processing device 120, the processor 210, and/or the CPU 340may execute the set of instructions, and when executing theinstructions, the processing device 120, the terminal 140, the processor210, and/or the CPU 340 may be configured to perform the process 3400.The operations of the illustrated process presented below are intendedto be illustrative. In some embodiments, the process 3400 may beaccomplished with one or more additional operations not described and/orwithout one or more of the operations discussed. Additionally, the orderof the operations of the process 3400 illustrated in FIG. 34 anddescribed below is not intended to be limiting.

Some embodiments of the present disclosure provide a method for imageprocessing to automatically determine one or more characteristic displayparameters for displaying an image. For example, a window technique maybe used to display normal tissues or lesions with differentdensities/signals via a window associated with CT values. One or morecharacteristic display parameters for display an image using the windowtechnique may include a window width (WW) and a window level (WL). Thewindow width may be a range of CT values (also referred to as CT valuerange) in a CT image. In the CT value range, some tissues and/or lesionswhose CT values are within the CT value range may be displayed indifferent simulated grayscale values in an image. Tissues and lesionswhose CT values exceed the CT value range may be displayed as whiteshadows or black shadows in the image, and have no difference ingrayscale. The window level may be the central position of the window.Since different tissues or lesions have different CT values, a suitablewindow width and window level may be determined for observing thedetails of a certain tissue to obtain improved display. For traditionalimage processing, the window width and window position may be usuallydetermined by an operator according to experience. When the window widthand window level cannot meet the requirements for image display, theoperator may need to adjust the window width and window level, whichincreases the workload of the doctor and decrease the accuracy.

In 3402, the processing device 120 (e.g., the acquisition module 3302)may obtain an image of a subject, the image including a target regionthat includes a representation of at least a portion the subject.

In some embodiments, the image may include a medical image. For example,the image may include a DR image, an MR image, a PET image, a CT image,etc. The subject may be biological or non-biological. For example, thesubject may include a patient, a man-made object, etc. As anotherexample, the subject may include a specific portion, organ, and/ortissue of a patient. As still another example, the subject may include abreast. More descriptions for the subject and the image may be foundelsewhere in the present disclosure (e.g., FIGS. 1 and 5, and thedescriptions thereof).

The target region may be an image region identified from the image. Thetarget region (e.g., a region of interest (ROI)) may be the focus ofimage analysis for further processing. For example, the target regionmay represent a tumor, one or more nodules, polyps, a lesion, etc.

In some embodiments, the target region may be determined from the imageusing an image processing technique (e.g., an image segmentationtechnique). The image processing may determine one or more specificregions with unique properties and determine a target region (e.g., aregion of interest) from the one or more specific regions. The imageprocessing technique may include using a threshold-based segmentationalgorithm, a region-based segmentation algorithm, an edge-basedsegmentation algorithm, a specific theory-based segmentation algorithm,etc.

In some embodiments, the target region may be determined based on anautomatic segmentation technique, a semi-automatic segmentationtechnique, etc. In some embodiments, the target region may be determinedby a user (e.g., a doctor, a technician) manually. For example, the usermay determine the outline or boundary of the target region from theimage manually.

In some embodiments, a threshold segmentation algorithm may be used toprocess the image to determine the target region. Using the thresholdsegmentation algorithm, pixels in an image may be classified intoseveral categories (or group) based on a segmentation threshold and thedifference in grayscale between a target and the background todistinguish the target and the background. By determining whether thecharacteristic attribute of each pixel in the image meets thesegmentation threshold, the target region or the background region inwhich one the pixel in the image belongs to may be determined, and thena grayscale image may be converted into a binary image. In someembodiments, the Otsu algorithm may be used for image processing. Insome embodiments, the segmentation threshold may be input by a useraccording to experience. In some embodiments, the segmentation thresholdmay be determined by the medical system 100 according to, for example,process 500, process 1000, or any other algorithms. The thresholdsegmentation algorithm may convert the grayscale image into a binaryimage, which may effectively distinguish the target from the background.For example, if a doctor wants to observe a tumor in the stomach throughan image, then a threshold segmentation algorithm may be used todistinguish a tumor region (i.e., the target region) from other tissuesin the stomach.

In some embodiments, the determining the target region may includeremoving a region representing air (also referred to as air region orbackground region). The air region may be a non-target region or adirect exposure region, i.e., an empty exposure region. The emptyexposure region in an image may be formed based on projection datagenerated by a detector in an imaging device (e.g., the medical device110) after receiving X-rays that only pass through the air beforereaches the detector. The target region may represent a subject (e.g., alesion or a tumor). In some embodiments, removing the air region fromthe image may include determining whether the image includes the airregion. For example, whether the image includes the air region may bedetermined according to process 1400 as illustrated in FIG. 14. Inresponse to a determination that the image includes the air region, theair region may be determined from the image. For example, the air region(i.e., the background region) may be determined from the image accordingto process 1000 as illustrated in FIG. 10. Then the air region may beremoved from the image. For example, gray values in the air region maybe set as “0” or “255” to remove the air region from the image.Accordingly, the interference of the air region with the determinationof the target region may be eliminated effectively, which is helpful fora user (e.g., the doctor) to make the medical diagnosis based on theimage.

In some embodiments, the processing device 120 may determine the targetregion from the image according to process 500 as described in FIG. 5.

In some embodiments, the processing device 120 may determine the targetregion from the image according to process 1000 as described in FIG. 10.

In 3404, the processing device 120 (e.g., the distribution of grayvalues module 3304) may determine a distribution of gray values ofpixels in the target region, the distribution indicating a count ofpixels corresponding to each gray value in the target region.

The distribution of the gray values of the pixels in the target regionmay indicate a count of pixels in the target region whose gray valuesare the same.

In some embodiments, the distribution of the gray values of the pixelsin the target region may include a grayscale histogram. A histogram is astatistical graph, which is represented by a series of vertical stripesor line segments with different heights. When the vertical stripes orline segments are densely distributed, the histogram may show a curve.The transverse axis of a histogram may indicate a data type (e.g., apixel parameter of a type), and the vertical axis may indicate thedistribution of the data type. In some embodiments, the transverse axisof the grayscale histogram of the target region may denote gray values,and the vertical axis may denote a count of pixels corresponding to eachof the gray values. The grayscale histogram of the target region may bedetermined by determining a count of pixels with each of the gray valuesin the target region and arranging the counts of pixels according to thegray values from small to large. As used herein, the gray values in thetarget region may refer to gray values of pixels in the target regionafter deduplication. The shape of the grayscale histogram may bedetermined based on the distribution of the gray values in the targetregion. If the gray values in the target region are more and thedifference in the count of pixels corresponding to each gray value issmall, the grayscale histogram may include multiple peaks, that is, thegrayscale histogram of the target region may be relatively wide and thepeaks may be not too steep. If the gray values in the target region arerelatively concentrated (i.e., gray values are close to each other), thegrayscale histogram may have a steep single-peak.

In 3406, the processing device 120 (e.g., the parameter determinationmodule 3306) may determine a characteristic display parameter for thetarget region based on the distribution of the gray values of the pixelsin the target region.

The characteristic display parameter may include at least one of awindow width or a window level for the target region.

In some embodiments, the processing device 120 may perform an iterativeprocess to determine at least one of a window width or a window levelfor the target region. The iterative process may include a plurality ofiterations. For each iteration of the iterative process, the processingdevice 120 may determine an accumulated count of pixels in a currentiteration by summing a count of pixels corresponding to a current grayvalue with an accumulated count of pixels determined in a prioriteration. The processing device 120 may determine whether theaccumulated count of pixels in the current iteration satisfies a currentcondition. The processing device 120 may designate a gray value in thetarget region as the current gray value in response to determining thatthe accumulated count of pixels in the current iteration does notsatisfy the condition. The processing device 120 may terminate theiterative process until the accumulated count of pixels in the currentiteration satisfies the current condition and determine, based at leastin part on the current gray value, the at least one of the window widthor the window level for the target region. For each iteration, the grayvalue and the accumulated count of pixels may be updated. The iterativeprocess may also be referred to as a statistic process. An iteration inthe iterative process may also be referred to as a statistic. The countof iterations may also be referred to as a count of statistics or acount of accumulations of pixels. More descriptions for the iterativeprocess may be found elsewhere in the present disclosure (e.g., FIG. 35,and the descriptions thereof).

In the iterative process, the processing device 120 may update thecondition with the increase of the accumulated count of pixels. Forexample, the condition may include a count threshold, the processingdevice 120 may decrease the count threshold with the increase of theaccumulated count of pixels. As another example, the processing device120 may update the current condition after a count of iterations (e.g.,1, 2, 3, etc.) are performed. With the increase of the accumulated countof pixels and the increase of the count of statistics, the accumulatedcount of pixels is easy to satisfy the condition, which may improve theaccuracy of the window, thereby avoiding some gray values to be removedand not displayed.

In some embodiments, the processing device 120 may divide the grayscalehistogram into multiple intervals and perform the iteration processbased on the multiple intervals. The count of pixels corresponding toeach gray value may be accumulated for each interval according to one ormore rules to obtain the accumulated count of pixels until theaccumulated count of pixels satisfies the condition (i.e., the currentcondition).

In some embodiments, if the grayscale histogram of the target region isdivided into the multiple intervals along the vertical axis, the countof pixels corresponding to each gray value may be accumulated from aninterval corresponding to a minimum height in the grayscale histogram toa next interval in sequence (i.e., from the bottom to top of thegrayscale histogram). If the accumulation of the count of pixelscorresponding to each gray value in a current interval is completed, andthe accumulated count of pixels does not satisfy the current condition,the count of pixels corresponding to each gray value in an adjacentinterval (i.e., a next interval) may be accumulated until theaccumulated count of pixels satisfies the current condition. For thecurrent interval for statistics, the count of pixels corresponding tothe gray values in the current interval may be accumulated from the leftside to the center and from the right side to the center of thegrayscale histogram in sequence. In other words, the count of pixelscorresponding to gray values in the current interval that is smallerthan a gray value corresponding to the center of the grayscale histogrammay be accumulated from small to large in sequence. The count of pixelscorresponding to gray values in the current interval that is greaterthan the gray value corresponding to the center of the grayscalehistogram may be accumulated from large to small in sequence. If theaccumulated count of pixels corresponding to a current gray value in acurrent interval that is located at the left side of the center of thehistogram satisfies the condition, the current gray value may bedesignated as a starting point (also referred to as a first end) of thewindow. A maximum gray value in the target region that belongs to thecurrent interval may be designated as an end point (also referred to asa second end) of the window. If the accumulated count of pixelscorresponding to a current gray value in a current interval that islocated at the right side of the center of the histogram satisfies thecondition, the current gray value may be designated as the end point ofthe window. A maximum gray value in the target region that belongs tothe current interval and located at the left side of the grayscalehistogram may be designated as the starting point of the window.

In some embodiments, if the grayscale histogram of the target region isdivided into the multiple intervals along the transverse axis, the countof pixels corresponding to each gray value may be accumulated from aninterval including a minimum gray value in the grayscale histogram. Forthe multiple intervals for statistics, the count of pixels correspondingto the gray values in the multiple intervals may be accumulated from theleft side to the center and from the right side to the center of thegrayscale histogram in sequence. If the accumulation of the count ofpixels corresponding to each gray value in a current interval that islocated at the left side of the grayscale histogram is completed, andthe accumulated count of pixels does not satisfy the current condition,the count of pixels corresponding to each gray value in a next intervalthat is located at the right side of the grayscale histogram may beaccumulated until the accumulated count of pixels satisfies the currentcondition. The count of pixels corresponding to gray values in thecurrent interval that is smaller than a gray value corresponding to thecenter of the grayscale histogram may be accumulated from small to largein sequence. The count of pixels corresponding to gray values in thecurrent interval that is greater than the gray value corresponding tothe center of the grayscale histogram may be accumulated from large tosmall in sequence. If the accumulated count of pixels corresponding to acurrent gray value in a current interval that is located at the leftside of the center of the histogram satisfies the condition, the currentgray value may be designated as a starting point of the window. Amaximum gray value in the target region that belongs to a next intervalmay be designated as an end point of the window. If the accumulatedcount of pixels corresponding to a current gray value in a currentinterval that is located at the right side of the center of thehistogram satisfies the condition, the current gray value may bedesignated as the end point of the window. A maximum gray value in thetarget region that belongs to a previous interval and located at theleft side of the grayscale histogram may be designated as the startingpoint of the window. More descriptions for the accumulation of the countof pixels may be found in FIGS. 35 and 36.

A difference between gray values corresponding to the starting point andthe end point may be designated as the window width. A center betweenthe gray values corresponding to the starting point and the end pointmay be designated as the window level.

In 3408, the processing device 120 (e.g., the display module 3308) maydisplay the image based on the characteristic display parameter.

In some embodiments, the image may be displayed in the form of grayvalues. To contain more organizational information, the data depth (bitdepth) of the image may be greater than 8 bit. For example, the datadepth (bit depth) may include 12 bit, 14 bit, 15 bit, 16 bit, etc. Insome embodiments, the bit depth of the image may exceed 8 bit, theprocessing device 120 may convert the image into a processed image witha bit depth of 8 bit based on the determined characteristic displayparameter (e.g., the window width and the window level).

According to process 3500, the window width and window level for displaythe target region of the image may be determined according to the grayvalue distribution without operations of a user (e.g., a doctor), whichimproves the accuracy of the window width and window level, and thewindow width and window level for display the target region of the imagedetermined according to the gray value distribution may be more suitablefor the target region with improved display, thereby improving theaccuracy for diagnosis based on the displayed target region. Especiallyfor digital X-ray photography (DR) imaging, an image may be influencedby the large volume of a subject to be imaged, the complex positioningof different parts of the subject, etc. The window width and windowlevel determined based on the above method for processing an image maygreatly improve the stability of display effect of the image and reducethe workload of an operator (e.g., the doctor) to adjust the windowwidth and window level twice.

It should be noted that the above description is merely provided forillustration, and not intended to limit the scope of the presentdisclosure. For persons having ordinary skills in the art, multiplevariations and modifications may be made under the teachings of thepresent disclosure. However, those variations and modifications do notdepart from the scope of the present disclosure. In some embodiments,one or more operations may be omitted and/or one or more additionaloperations may be added.

FIG. 35 is a flowchart illustrating an exemplary process for aniterative process for determining a window width or a window level forthe target region according to some embodiments of the presentdisclosure. In some embodiments, process 3500 may be implemented as aset of instructions stored in the storage device 130, storage 220, orstorage 390. The processing device 120, the processor 210 and/or the CPU340 may execute the set of instructions, and when executing theinstructions, the processing device 120, the terminal 140, the processor210 and/or the CPU 340 may be configured to perform the process 3500.The operations of the illustrated process presented below are intendedto be illustrative. In some embodiments, the process 3500 may beaccomplished with one or more additional operations not described and/orwithout one or more of the operations discussed. Additionally, the orderof the operations of the process 3500 illustrated in FIG. 35 anddescribed below is not intended to be limiting. The iterative processmay include multiple iterations. For illustration purposes, a currentiteration of the iterations is described in the following description.The current iteration may include one or more operations of the process3500.

In 3502, the processing device 120 may divide a grayscale histogram of atarget region of an image into multiple intervals. The grayscalehistogram may indicate a gray value distribution of pixels in the targetregion. The grayscale histogram may represent a count of pixels whosegray values are the same in the target region. The grayscale histogrammay include a transverse axis and a vertical axis. In some embodiments,the transverse axis may indicate gray values and the vertical axis mayindicate a count of pixels. In some embodiments, the transverse axis mayindicate a count of pixels and the vertical axis may indicate grayvalues. The following descriptions are provided with reference to thetransverse axis of the grayscale histogram as the gray values and thevertical axis of the grayscale histogram as the count of pixels, unlessotherwise stated. It is understood that this is for illustrationpurposes and not intended to be limiting.

Each of the multiple intervals may include multiple gray values in thetarget region and a count of pixels corresponding to each of themultiple gray values.

In some embodiments, the processing device 120 may divide the grayscalehistogram into the multiple intervals along the vertical axis. As usedherein, the division of the grayscale histogram along the vertical axismay refer to dividing the grayscale histogram via dividing the verticalaxis into multiple ranges. Each of the multiple intervals may include arange of counts of pixels of certain gray values. For example, FIG. 36shows an exemplary grayscale histogram according to some embodiments ofthe present disclosure. As shown in FIG. 36, the grayscale histogram isdivided into multiple intervals (e.g., a first interval, a secondinterval, a third interval, . . . , an Nth interval, etc.) along thevertical axis. Each interval may correspond to a range of the count ofpixels (e.g., 0-500, 500-1000, 1000-1500, etc.) The heights of the firstinterval, the second interval, the third interval, . . . , the Nthinterval may be increased in sequence. As used herein, the height of aninterval may refer to a maximum count of pixels in the interval.

In some embodiments, the processing device 120 may determine a totalcount of pixels in the target region and divide the grayscale histograminto the multiple intervals along the vertical axis based on the totalcount of pixels in the target region. For example, the processing device120 may determine a count of the multiple intervals, and divide thegrayscale histogram into the multiple intervals along the vertical axisbased on the total count of pixels and the count of the multipleintervals.

In some embodiments, the processing device 120 may determine a maximumcount of pixels in the vertical axis (or the height of the highestpoint) in the grayscale histogram of the target region and divide thegrayscale histogram into the multiple intervals along the vertical axisbased on the maximum count of pixels in the vertical axis and the countof the multiple intervals. The count of the multiple intervals may beset according to the maximum count of pixels corresponding to a grayvalue in the target region.

In some embodiments, the multiple intervals may have the same widthalong the vertical axis, that is, the grayscale histogram may be dividedinto equal parts in the vertical direction. As used herein, the width ofan interval refers to a difference between a maximum count of pixels anda minimum count of pixels in the interval. For example, if the maximumcount of pixels in the vertical axis in the grayscale histogram is 8000,the maximum count 8000 may be divided into equal parts. The width ofeach part may be used as a step height. As a further example, if themaximum count of 8000 is divided into 100 equal parts, the height ofeach equal part is 80, that is, the step height is 80. As still anotherexample, as shown in FIG. 36, the maximum count of pixels corresponds toa gray value X, and the maximum count of pixels was divided into N equalparts. Each equal part may be designated as an interval, and eachinterval has the same width.

In some embodiments, the multiple intervals may have different widthsalong the vertical axis. For example, the widths of the multipleintervals may decrease with the increase of the count of pixels. Asanother example, the maximum count of pixels may be divided into unequalparts.

In some embodiments, the processing device 120 may divide the grayscalehistogram along the transverse axis into the multiple intervals. As usedherein, the division of the grayscale histogram along the transverseaxis may refer to dividing the grayscale histogram via dividing thetransverse axis into multiple ranges. For example, FIG. 39 shows anexemplary grayscale histogram according to some embodiments of thepresent disclosure. As shown in FIG. 39, the grayscale histogram isdivided into multiple intervals (e.g., a first interval, a secondinterval, . . . , an (X−1)th interval, an Xth interval, etc.) along thetransverse axis. Each interval may correspond to a range of gray values(e.g., 1000-1500, 1500-1800, 1800-2000, etc.).

In some embodiments, the processing device 120 may divide the grayscalehistogram along the transverse axis into the multiple intervals based ona total count of pixels or the total count of effective pixels in thegrayscale histogram of the target region. The effective pixels in thegrayscale histogram of the target region refers to pixels obtained afterthe preprocessing of the target region of the image or the grayscalehistogram of the target region.

For example, the preprocessing of the grayscale histogram may includeremoving gray values within a range (e.g., a range on the left and rightsides of the grayscale histogram) to form an effective region of thegrayscale histogram. As shown in FIG. 39, region SS and region SS1 inFIG. 39 are removed by the preprocessing process. In some embodiments,some unstable points exist in the grayscale histogram of the targetregion, and the gray values in the ranges on the left and right sides ofthe histogram may be removed to avoid interference of these unstablepoints. For example, the count of pixels corresponding to the grayvalues within the range (e.g., a range on the left and right sides ofthe grayscale histogram) that are deleted may be of a proportion of thetotal count of the pixels in the target region. The proportion may bewithin 5% of the total count of pixels in the target region. As anotherexample, the count of gray values within the range (e.g., a range on theleft and right sides of the grayscale histogram) that are deleted may beof a proportion of the total count of the gray values in the targetregion. For example, 1% of gray values may be removed on the left andright sides of the grayscale histogram. By removing some unstable pointsin the grayscale histogram of the target region, the display effect ofthe image displayed according to the window width and window level maybe more stable, which may improve the accuracy of medical diagnosis.

Each of the multiple intervals may include a range of gray values and acount of pixels corresponding to each gray value in the range. In someembodiments, each interval may have different width of a range of grayvalues along the transverse axis. In some embodiments, each interval mayhave the same width of a range of gray values along the transverse axis.In other words, a difference between a maximum gray value and a minimumgray value corresponding to each interval may be the same. In someembodiments, each interval may correspond to the same count of pixels.In other words, the count of pixels in the target region whose grayvalues are in each interval may be the same. The count of the multipleintervals may be set according to the total count of pixels in thetarget region. For example, if the total count of pixels of 80,000 inthe target region is divided into 100 equal parts, then the count ofpixels in each equal part is 800, that is, the count of pixels in eachinterval is 800. In other embodiments, it may also be divided intomultiple intervals with an unequal count of pixels according to the graydistribution of the target region.

In 3504, the processing device 120 (e.g., the parameter determinationmodule 3306) may determine an accumulated count of pixels in a currentiteration by summing a count of pixels corresponding to a current grayvalue with an accumulated count of pixels determined in a prioriteration. The current gray value may refer to a gray value in thetarget region that belong to a current interval. In some embodiments,when the current iteration is a first iteration, if the grayscalehistogram is divided along the vertical axis, the current interval maybe an interval located at the bottom-most of the grayscale histogramwith a minimum height; if the grayscale histogram is divided along thetransverse axis, the current interval may be an interval located at theleft most of the grayscale histogram with a minimum range of grayvalues.

In some embodiments, if the grayscale histogram is divided along thevertical axis, the current interval may include gray values in thetarget region that are located at the left side of the center of thegrayscale histogram and gray values in the target region that is locatedat the right side of the center of the grayscale histogram. The grayvalues in the target region that are located at the left side of thecenter of the grayscale histogram may be less than a gray valuecorresponding to the center of the grayscale histogram and the grayvalues that are located at the right side of the center of the grayscalehistogram may exceed the gray value corresponding to the center of thegrayscale histogram. As shown in FIG. 37, the first interval includesgray values “a” and “b” that are located at the left side of the centerof the grayscale histogram and gray value “c” that are located at theright side of the center of the grayscale histogram. If the current grayvalue is located at the left side of the center of the grayscalehistogram, the current gray value in the current iteration may exceed aprior gray value in the current interval used in the prior iteration. Ifthe current gray value is located at the right side of the center of thegrayscale histogram, the current gray value in the current iteration maybe less than a prior gray value in the current interval in the prioriteration. In other words, the processing device 120 may accumulate thecount of pixels corresponding to each gray value in the current intervalfrom the left side to the center of the grayscale histogram and from theright side to the center of the grayscale histogram in sequence.

In some embodiments, if the grayscale histogram is divided along thetransverse axis, the current interval may include gray values in thetarget region that are located at the left side of the center of thegrayscale histogram or gray values in the target region that are locatedat the right side of the center of the grayscale histogram. As shown inFIG. 39, the first interval includes gray values that are located at theleft side of the center of the grayscale histogram and the Xth intervalincludes gray values that are located at the right side of the center ofthe grayscale histogram. If the current interval is located at the leftside of the center of the grayscale histogram, the current gray value inthe current iteration may exceed a prior gray value in the currentinterval used in the prior iteration. If the current interval is locatedat the right side of the center of the grayscale histogram, the currentgray value in the current iteration may be less than a prior gray valuein the current interval in the prior iteration. In other words, theprocessing device 120 may accumulate the count of pixels correspondingto each gray value in the current interval from the left side to thecenter of the grayscale histogram or from the right side to the centerof the grayscale histogram in sequence.

In 3506, the processing device 120 (e.g., the parameter determinationmodule 3306) may determine whether the accumulated count of pixels inthe current iteration satisfies a current condition.

In response to a determination that the accumulated count of pixels inthe current iteration does not satisfy a current condition, theprocessing device 120 may proceed to perform operation 3508 andoperation 3510.

In response to a determination that the accumulated count of pixels inthe current iteration satisfies a current condition, the processingdevice 120 may proceed to perform operation 3512.

In some embodiments, the current condition may include a current countthreshold. Whether the accumulated count of pixels in the currentiteration satisfies the current condition may include whether theaccumulated count of pixels in the current iteration exceeds or equalsthe current count threshold. If the accumulated count of pixels in thecurrent iteration exceeds or equals the current count threshold, theaccumulated count of pixels in the current iteration may satisfy thecurrent condition.

In some embodiments, when the current iteration is a first iteration,the current count threshold may be an initial count threshold. Theinitial count threshold may be set by a user or according to a defaultsetting of the medical system 100. For example, the initial thresholdmay be set to 5 to 20% of the total count of pixels in the grayscalehistogram or the preprocessed grayscale histogram. As a further example,the initial threshold may be set to 15% of the total count of pixels inthe grayscale histogram or the preprocessed grayscale histogram.

In some embodiments, the current condition may be the same as a priorcondition in a prior iteration. In some embodiments, the currentcondition may be different from a prior condition in a prior iteration.For example, the current count threshold may be less than a prior countthreshold in the prior iteration.

In 3508, the processing device 120 (e.g., the parameter determinationmodule 3306) may designate a gray value in the target region as thecurrent gray value.

In some embodiments, the processing device 120 may determine the grayvalue according to one or more rules.

In some embodiments, if the grayscale histogram is divided along thevertical axis, the one or more rules may include at least one of a firstrule or a second rule. The first rule may include determining aninterval among the multiple intervals from a bottom interval to a topinterval along the vertical axis in sequence. In other words, if thecurrent gray value in the current iteration is a last one gray value tobe accumulated in the current interval, the processing device 120 maydesignate a gray value (e.g., a first gray value) in a next intervaladjacent to the current interval as the current gray value. For example,if gray values in the current interval are distributed at two sides ofthe center of the transvers axis or all the gray values are distributedat the right side of the center of the transvers axis, the at least onegray value to be accumulated in the current interval may be a minimumgray value in the current interval that is located on the right side ofthe center of the transvers axis; if all the gray values are distributedat the left side of the center of the transvers axis, the at least onegray value to be accumulated in the current interval may be a maximumgray value in the current interval.

The second rule may relate to determining the gray value in the currentinterval from two sides to a center of the transverse axis in sequence(e.g., from small to large or from large to small). If the current grayvalue in the current iteration is not a last one gray value to beaccumulated in the current interval, the processing device 120 maydesignate a gray value in the current interval as the current grayvalue. Further, if the current gray value is not the last one gray valueto be accumulated in the current interval at the left side of the centerof the grayscale histogram, the processing device 120 may update thecurrent gray value based on a gray value that is located at the leftside of the center of the grayscale histogram in the current intervaland exceeds the current gray value. If the current gray value is not thelast one gray value in the current interval at the right side of thecenter of the grayscale histogram, the processing device 120 may updatethe current gray value based on a gray value that is located at theright side of the center of the grayscale histogram in the currentinterval and is less than the current gray value. In other words, theprocessing device 120 may accumulate the count of pixels correspondingto each gray value in the current interval from the left side to thecenter of the grayscale histogram and from the right side to the centerof the grayscale histogram in sequence.

As shown in FIG. 36, in multiple iterations, the processing device 120may update the current gray value based on gray values in the firstinterval of the multiple intervals from a left side to the center of thetransverse axis in sequence (i.e., from small to large) and from a rightside to the center of the transverse axis when the current gray value isa last one gray value at the left side of the grayscale histogram in thefirst interval.

In some embodiments, if the grayscale histogram is divided along thetransverse axis, the first rule may relate to determining an intervalfrom the multiple intervals from two sides to a center of the transverseaxis in sequence, and the second rule may relate to determining the grayvalue in the interval from the left side to the center of the transverseaxis or from the right side to the center of the transverse axis insequence. In other words, if the current gray value in the currentiteration is a last one gray value (i.e., a maximum gray value) in thecurrent interval that is located at the left side of the grayscalehistogram, the processing device 120 may designate a gray value (e.g., amaximum gray value) in a next interval that is located at the right sideof the grayscale histogram as the current gray value. If the currentgray value in the current iteration is a last one gray value (i.e., amaximum gray value) in the current interval that is located at the rightside of the grayscale histogram, the processing device 120 may designatea gray value (e.g., a minimum gray value) in a next interval that islocated at the left side of the grayscale histogram as the current grayvalue. If the current gray value is not the last one gray value in thecurrent interval that is located at the right side of the center of thegrayscale histogram, the processing device 120 may update the currentgray value based on a gray value that is located at the right side ofthe center of the grayscale histogram in the current interval and isless than the current gray value. If the current gray value is not thelast one gray value in the current interval that is located at the leftside of the center of the grayscale histogram, the processing device 120may update the current gray value based on a gray value that is locatedat the left side of the center of the grayscale histogram in the currentinterval and exceeds the current gray value.

As shown in FIG. 39, in multiple iterations, when the current intervalis the first interval, the processing device 120 may update the currentgray value based on gray values in the first interval of the multipleintervals from the left side to the right side in sequence (i.e., fromsmall to large). If the accumulated count of pixels in the currentiteration does not satisfy the current condition when counts of pixelscorresponding to all gray values in the first interval are accumulated,the processing device 120 may update the current gray value based ongray values in the Xth interval of the multiple intervals from the rightside to the left side in sequence (i.e., from large to small). If theaccumulated count of pixels in the current iteration does not satisfythe current condition when counts of pixels corresponding to all grayvalues in the Xth interval are accumulated, the processing device 120may update the current gray value based on gray values in the secondinterval of the multiple intervals from the left side to the right sidein sequence (i.e., from small to larger) until the current condition issatisfied.

In 3510, the processing device 120 (e.g., the parameter determinationmodule 3306) may update the current condition.

In some embodiments, updating the current condition may include updatingthe current count threshold. In some embodiments, the processing device120 may update the current count threshold by decreasing the currentcount threshold with a reduction. In some embodiments, the reduction ofthe current count threshold may be different in each iteration. In someembodiments, the reduction of the current count threshold may be thesame in each iteration. In some embodiments, the reduction of thecurrent count threshold in multiple iterations associated with grayvalues in the same interval may be the same.

In some embodiments, the processing device 120 may determine thereduction of the current count threshold for the current iteration basedon the range of gray values (also referred to as grayscale distributionrange of the grayscale histogram) in the target region (or thepreprocessed target region) and/or and the altitude climb information ofthe grayscale histogram. In some embodiments, the grayscale distributionrange of the grayscale histogram refers to a range from the minimum grayvalue to the maximum gray value in the grayscale histogram (or thepreprocessed grayscale histogram). The altitude climb information mayindicate a steep degree of the peaks in the grayscale histogram. Byupdating the count threshold according to the gray distribution range ofthe grayscale histogram and the altitude climb information of thegrayscale histogram, the count threshold may be updated according to thespecific shape of the grayscale histogram, which may ensure the updatedcount threshold as close as possible to an ideal value.

In some embodiments, the count threshold may be updated based on thegrayscale distribution range of the grayscale histogram and the altitudeclimb information of the grayscale histogram, which may be applicablefor a grayscale histogram with different shapes, for example, a shapeincluding a sharp single peak or a shape including a multi-peak with agentle large span. In some embodiments, the count threshold may beupdated according to the shape of the grayscale histogram. For example,when the grayscale histogram includes a sharp single-peak, the reductionof the count threshold in each iteration may be determined only based onthe altitude climb information of the histogram, and if the grayscalehistogram has a gentle multi-peak, the reduction of the count thresholdin each iteration may be determined according to the gray distributionrange of the grayscale histogram.

In some embodiments, if the grayscale histogram is divided into themultiple intervals along the vertical axis, the reduction of the countthreshold may be determined according to the grayscale distributionrange and the altitude climb information according to Equation (13) asfollows:

$\begin{matrix}{{{A1} = {{A2} - \frac{S \times \frac{n}{N}}{{C1} - {C2}}}},} & (13)\end{matrix}$

where A1 denotes the updated count threshold, A2 denotes the currentcount threshold, S denotes the total count of pixels or effective pixelsin the grayscale histogram, n denotes a count of intervals in whichcounts of pixels have been accumulated, N denotes the total count of themultiple intervals, C1 denotes the maximum gray value of the grayscalehistogram, and C2 denotes the minimum gray value of the grayscalehistogram. n/N corresponds to the climbing height information (e.g., thesteep degree) of the grayscale histogram. (C1-C2) denotes the graydistribution range. (A2−A1) denotes the reduction of the count thresholdin the current iteration. According to Equation (9), in the sameinterval, the reduction of the count threshold (A2−A1) may be constant,and in different intervals, the reduction of the count threshold (A2−A1)may be variable as the count of intervals in which counts of pixels havebeen accumulated changes. Specifically, in the same interval, in eachiteration, the current threshold may be updated by decreasing with thesame reduction and for different intervals, the reduction of the countthreshold may be different. Specifically, along the vertical axis, inthe interval with a lower height, the reduction of the count thresholdmay be smaller, and in the interval with a higher height, the reductionof the count threshold may be greater, thereby speeding up thetermination of the iterative process.

In some embodiments, if the grayscale histogram is divided into themultiple intervals along the transverse axis, the reduction of the countthreshold may be determined based on the gray distribution range and thedegree of contraction toward the center of the grayscale histogram. Thedegree of contraction toward the center of the grayscale histogram mayindicate a concentration degree of the distribution of gray values.Further, the processing device 120 may determine the reduction of thecount threshold according to Equation (14) as follows:

$\begin{matrix}{{{B1} = {{B2} - \frac{Z \times \frac{x}{X}}{{D1} - {D2}}}},} & (14)\end{matrix}$

where B1 denotes the updated count threshold, B2 denotes the currentcount threshold before updating, Z denotes the total count of pixels oreffective pixels in the grayscale histogram, x denotes a count ofintervals in which counts of pixels have been accumulated in the currentiteration, X denotes the total count of the multiple intervals, D1denotes the maximum gray value of the grayscale histogram, and D2denotes the minimum gray value of the grayscale histogram. x/X indicatesthe degree of contraction toward the center. (D1-D2) denotes the graydistribution range. (B2−B1) denotes the reduction of the count thresholdin the current iteration.

In 3512, the processing device 120 (e.g., the parameter determinationmodule 3306) may terminate the iterative process.

In response to the determination that the accumulated count of pixels inthe current iteration equals or exceeds the count threshold, theprocessing device 120 may terminate the iterative process.

In 3513, the processing device 120 (e.g., the parameter determinationmodule 3306) may determine the at least one of the window width or thewindow level for the target region based on at least in part on thecurrent gray value.

In some embodiments, when the grayscale histogram is divided along thevertical axis, if the current interval is located at the left side ofthe center of the histogram, the current gray value may be designated asa starting point of the window. A maximum gray value in the targetregion that belongs to the current interval may be designated as an endpoint of the window. If the current interval is located at the rightside of the center of the grayscale histogram, the current gray valuemay be designated as the end point of the window. A maximum gray valuein the target region that belongs to the current interval and is locatedat the left side of the grayscale histogram may be designated as thestarting point of the window. For example, FIGS. 37 and 38 show anexemplary grayscale histogram according to some embodiments of thepresent disclosure. As shown in FIG. 37, the maximum count of pixels inthe current interval is 1800. The accumulated count of pixels in thecurrent interval on the left side of the grayscale histogram does notreach the count threshold and the gray value of 1294 is the last onegray value (i.e., a maximum gray value) in the current interval at theleft side of the grayscale histogram. Then, the accumulation of thecount of pixels may be performed from the right side to the center inthe current interval and when the gray value is of 3166, the accumulatedcount of pixels in the current interval equals or exceeds the countthreshold. The gray value of 1294 may be designated as the startingpoint of the window and the gray value of 3166 may be designated as theending point of the window which are determined according to process3500 based on the changed count threshold. As shown in FIG. 38, thestarting point of the window corresponds to the gray value 1316, and theend point of the window corresponds to the gray value 3006 which aredetermined based on the unchanged count threshold. The gray value of theend point of the window on the right side of the grayscale histogram inFIG. 38 is smaller than the gray value of the end point of the window onthe right side of the grayscale histogram in FIG. 37, i.e., the endpoint in FIG. 38 goes more toward the center than that in FIG. 37, whichmay cause pixels of the image whose gray values exceed the gray value ofthe end point of the window to be not visible or unclear. The updatedcount threshold in this disclosure may retain the part of the removedimage that the user wants to see. It can avoid that the grayscaleoriginally expected to be displayed is removed and not displayed, toensure that the final display image may meet the needs, and the doctordoes not need to perform the second adjustment process.

In some embodiments, when the grayscale histogram is divided along thetransverse axis, if the current interval is located at the left side ofthe center of the histogram, the current gray value may be designated asa starting point of the window. A maximum gray value in the targetregion that belongs to a next interval that is located at the right sideof the center of the histogram may be designated as an end point of thewindow. If the current interval is located at the right side of thecenter of the histogram, the current gray value may be designated as theend point of the window. A maximum gray value in the target region thatbelongs to a previous interval and located at the left side of thegrayscale histogram may be designated as the starting point of thewindow. A difference between gray values corresponding to the startingpoint and the end point may be designated as the window width. A centerbetween the gray values corresponding to the starting point and the endpoint may be designated as the window level (i.e., the center of thewindow width). For example, if the end point on the left side of thegrayscale histogram is 1000 and the end point on the right side of thegrayscale histogram is 4000, then the window width is 3000 and thewindow level is 2500.

Accordingly, although the initial count threshold also needs to be setaccording to experience at the beginning of the iterative process, thecount threshold may be updated as closer and closer to the ideal valuebased on the gray value distribution of the target region in an image,which may improve the accuracy of the characteristic display parameter,thereby improving the display of the target region without the need fora second adjustment by an operator.

It should be noted that the above description is merely provided forillustration, and not intended to limit the scope of the presentdisclosure. For persons having ordinary skills in the art, multiplevariations and modifications may be made under the teachings of thepresent disclosure. However, those variations and modifications do notdepart from the scope of the present disclosure. In some embodiments,one or more operations may be omitted and/or one or more additionaloperations may be added.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure, or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “unit,” “module,” or “system.” Furthermore, aspects ofthe present disclosure may take the form of a computer program productembodied in one or more computer-readable media having computer-readableprogram code embodied thereon.

A non-transitory computer-readable signal medium may include apropagated data signal with computer readable program code embodiedtherein, for example, in baseband or as part of a carrier wave. Such apropagated signal may take any of a variety of forms, includingelectromagnetic, optical, or the like, or any suitable combinationthereof. A computer-readable signal medium may be any computer-readablemedium that is not a computer-readable storage medium and that maycommunicate, propagate, or transport a program for use by or inconnection with an instruction execution system, apparatus, or device.Program code embodied on a computer-readable signal medium may betransmitted using any appropriate medium, including wireless, wireline,optical fiber cable, RF, or the like, or any suitable combination of theforegoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object-oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET,Python, or the like, conventional procedural programming languages, suchas the “C” programming language, Visual Basic, Fortran, Perl, COBOL,PHP, ABAP, dynamic programming languages such as Python, Ruby, andGroovy, or other programming languages. The program code may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider) or in a cloud computing environment or offered as aservice such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations, therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose and that the appended claimsare not limited to the disclosed embodiments, but, on the contrary, areintended to cover modifications and equivalent arrangements that arewithin the spirit and scope of the disclosed embodiments. For example,although the implementation of various components described above may beembodied in a hardware device, it may also be implemented as asoftware-only solution, e.g., an installation on an existing server ormobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereofto streamline the disclosure aiding in the understanding of one or moreof the various inventive embodiments. This method of disclosure,however, is not to be interpreted as reflecting an intention that theclaimed object matter requires more features than are expressly recitedin each claim. Rather, inventive embodiments lie in less than allfeatures of a single foregoing disclosed embodiment.

In some embodiments, the numbers expressing quantities, properties, andso forth, used to describe and claim certain embodiments of theapplication are to be understood as being modified in some instances bythe term “about,” “approximate,” or “substantially.” For example,“about,” “approximate” or “substantially” may indicate ±20% variation ofthe value it describes, unless otherwise stated. Accordingly, in someembodiments, the numerical parameters set forth in the writtendescription and attached claims are approximations that may varydepending upon the desired properties sought to be obtained by aparticular embodiment. In some embodiments, the numerical parametersshould be construed in light of the number of reported significantdigits and by applying ordinary rounding techniques. Notwithstandingthat the numerical ranges and parameters setting forth the broad scopeof some embodiments of the application are approximations, the numericalvalues set forth in the specific examples are reported as precisely aspracticable.

Each of the patents, patent applications, publications of patentapplications, and other material, such as articles, books,specifications, publications, documents, things, and/or the like,referenced herein is hereby incorporated herein by this reference in itsentirety for all purposes, excepting any prosecution file historyassociated with same, any of same that is inconsistent with or inconflict with the present document, or any of same that may have alimiting effect as to the broadest scope of the claims now or laterassociated with the present document. By way of example, should there beany inconsistency or conflict between the description, definition,and/or the use of a term associated with any of the incorporatedmaterial and that associated with the present document, the description,definition, and/or the use of the term in the present document shallprevail.

In closing, it is to be understood that the embodiments of theapplication disclosed herein are illustrative of the principles of theembodiments of the application. Other modifications that may be employedmay be within the scope of the application. Thus, by way of example, butnot of limitation, alternative configurations of the embodiments of theapplication may be utilized in accordance with the teachings herein.Accordingly, embodiments of the present application are not limited tothat precisely as shown and described.

What is claimed is:
 1. A system, comprising: at least one storage devicestoring executable instructions, and at least one processor incommunication with the at least one storage device, when executing theexecutable instructions, causing the system to perform operationsincluding: obtaining an image of a subject; determining a plurality ofimage blocks in the image; extracting grayscale features from each ofthe plurality of image blocks; determining, based on the grayscalefeatures, a segmentation threshold; and segmenting, based on thesegmentation threshold, the image.
 2. The system of claim 1, wherein todetermine a plurality of image blocks in the image, the at least oneprocessor is configured to cause the system to perform the operationsincluding: determining a first count of first lines along a firstdirection; determining a second count of second lines along a seconddirection; and dividing, based on the first count of first lines and thesecond count of second lines, the image into the plurality of imageblocks.
 3. The system of claim 1, wherein the grayscale features includea grayscale feature of a first type and a grayscale feature of a secondtype, and to determine, based on the grayscale features, a segmentationthreshold, the at least one processor is configured to cause the systemto perform the operations including: determining, based on the grayscalefeatures extracted from each of the plurality of image blocks, arelationship between the grayscale feature of the first type and thegrayscale feature of the second type; and determining, based on therelationship, the segmentation threshold.
 4. The system of claim 3,wherein the grayscale feature of the first type includes a mean ormedian of gray values of pixels in an image block, and the grayscalefeature of the second type includes a standard deviation or a varianceof the gray values of the pixels in the image block.
 5. The system ofclaim 3, wherein to determine a relationship between the grayscalefeature of the first type and the grayscale feature of the second type,the at least one processor is configured to cause the system to performthe operations including: determining, based on the grayscale features,a plurality of points in a coordinate system associated with thegrayscale feature of the first type and the grayscale feature of thesecond type, coordinates of each of the plurality of points representingthe grayscale features extracted from one of the plurality of imageblocks; and determining, based on at least a portion of the plurality ofpoints, the relationship between the grayscale feature of the first typeand the grayscale feature of the second type using a fitting technique.6. The system of claim 5, wherein the segmenting, based on thesegmentation threshold, the image including determining one or moretarget regions from the images, and the fitting technique is determinedbased on a count of the one or more target regions.
 7. The system ofclaim 5, wherein the at least one processor is configured to cause thesystem to perform the operations including: determining the at least aportion of the plurality of points by performing at least one of adownsampling operation or an operation for removing abnormal points. 8.The system of claim 7, wherein the operation for removing abnormalpoints includes: determining a range of grayscale features of the firsttype of the plurality of points; dividing the range of the grayscalefeatures of the first type into multiple sub-ranges; for each of themultiple sub-ranges, determining a first mean of grayscale features ofthe second type of points each of whose grayscale feature of the firsttype is in the sub-range; classifying, based on the first mean, thepoints into multiple groups; determining a second mean of grayscalefeatures of the second type of points in each of the multiple groups;and determining, based on the second mean, the abnormal points.
 9. Thesystem of claim 8, wherein the multiple groups include a first group anda second group, the first group including points each of whose grayscalefeature of the second type exceeds the first mean, and the second groupincluding points each of whose grayscale feature of the second type isless than the first mean; an abnormal point in the first group includesthe grayscale feature of the second type that exceeds the second mean;and an abnormal point in the second group includes the grayscale featureof the second type that is less than the second mean.
 10. The system ofclaim 3, wherein to determine, based on the relationship, thesegmentation threshold, the at least one processor is configured tocause the system to perform the operations including: determining, basedon the relationship, a value of the grayscale feature of the first typewhen the grayscale feature of the second type is minimum or maximum; anddesignating the value of the grayscale feature of the first type as thesegmentation threshold.
 11. The system of claim 1, wherein thesegmenting, based on the segmentation threshold, the image including:determining, based on the segmentation threshold, a target region thatincludes a representation of the subject; and the at least one processoris configured to cause the system to perform the operations including:determining a distribution of gray values of pixels in the targetregion, the distribution indicating a count of pixels corresponding toeach gray value in the target region; determining, based on thedistribution of the gray values of the pixels in the target region, acharacteristic display parameter for the target region; and displaying,based on the characteristic display parameter, the image.
 12. The systemof claim 11, wherein the characteristic display parameter includes awindow width or a window level for the target region.
 13. The system ofclaim 12, wherein to determine, based on the distribution of the grayvalues of the pixels in the target region, at least one of a windowwidth or a window level for the target region, the at least oneprocessor is configured to cause the system to perform the operationsincluding: performing an iterative process including a plurality ofiterations, for each iteration of the iterative process, determining anaccumulated count of pixels in a current iteration by summing a count ofpixels corresponding to a current gray value with an accumulated countof pixels determined in a prior iteration; determining whether theaccumulated count of pixels in the current iteration satisfies a currentcondition; in response to determining that the accumulated count ofpixels in the current iteration does not satisfy the condition,designating a gray value in the target region as the current gray value;and updating the current condition; terminating the iterative processuntil the accumulated count of pixels in the current iteration satisfiesthe current condition; and determining, based at least in part on thecurrent gray value, the at least one of the window width or the windowlevel for the target region.
 14. The system of claim 13, wherein thedistribution of the gray values of the pixels in the target regionincludes a grayscale histogram that includes a transverse axis denotingthe gray values and a vertical axis denoting a count of pixelscorresponding to each of the gray values.
 15. The system of claim 14,wherein to perform an iterative process, the at least one processor isconfigured to cause the system to perform the operations including:dividing the grayscale histogram along the vertical axis into multipleintervals, each of the multiple intervals including a range of counts ofpixels of certain gray values; and the designating a gray value in thetarget region as the current gray value including: determining the grayvalue according to at least one of a first rule or a second rule,wherein the first rule relates to determining an interval from themultiple intervals from a bottom interval to a top interval along thevertical axis in sequence, and the second rule relates to determiningthe gray value in the interval from two sides to a center of thetransverse axis.
 16. The system of claim 15, wherein to determine, basedat least in part on the current gray value, the at least one of thewindow width or the window level for the target region, the at least oneprocessor is configured to cause the system to perform the operationsincluding: determining that the current gray value is located on a leftside of the center of the transverse axis; designating the current grayvalue as a first end of the window width if the current gray value islocated on the left side of the center of the transverse axis; anddesignating a gray value corresponding to a same interval as the currentgray value as a second end of the window width, the second end of thewindow width being located on a right side of the center of thetransverse axis.
 17. The system of claim 15, wherein the currentcondition includes a count threshold, and the updating the currentcondition includes: decreasing, based on a range of the gray values inthe grayscale histogram, a total count of pixels in the grayscalehistogram, and an interval corresponding to the current gray value, anda count of the multiple intervals, the count threshold.
 18. A methodimplemented on a computing device including at least a processor and astorage device, the method comprising: obtaining an image of a subject;determining a plurality of image blocks in the image; extractinggrayscale features from each of the plurality of image blocks;determining, based on the grayscale features, a segmentation threshold;and segmenting, based on the segmentation threshold, the image.
 19. Themethod of claim 18, wherein the segmenting, based on the segmentationthreshold, the image including: determining, based on the segmentationthreshold, a target region that includes a representation of thesubject; and the at least one processor is configured to cause thesystem to perform the operations including: determining a distributionof gray values of pixels in the target region, the distributionindicating a count of pixels corresponding to each gray value in thetarget region; determining, based on the distribution of the gray valuesof the pixels in the target region, a characteristic display parameterfor the target region; and displaying, based on the characteristicdisplay parameter, the image.
 20. A non-transitory computer readablemedium storing instructions, the instructions, when executed by at leastone processor, causing the at least one processor to implement a methodcomprising: obtaining an image of a subject; determining a plurality ofimage blocks in the image; extracting grayscale features from each ofthe plurality of image blocks; determining, based on the grayscalefeatures, a segmentation threshold; and segmenting, based on thesegmentation threshold, the image.